-
Notifications
You must be signed in to change notification settings - Fork 6
/
lstm2d_test.py
85 lines (71 loc) · 2.92 KB
/
lstm2d_test.py
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
# Copyright 2016 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 2D LSTMs."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import test_util
lstm2d = tf.contrib.ndlstm.lstm2d
def _rand(*size):
return np.random.uniform(size=size).astype("f")
class Lstm2DTest(test_util.TensorFlowTestCase):
def testImagesToSequenceDims(self):
with self.test_session():
inputs = tf.constant(_rand(2, 7, 11, 5))
outputs = lstm2d.images_to_sequence(inputs)
tf.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (11, 14, 5))
def testSequenceToImagesDims(self):
with self.test_session():
inputs = tf.constant(_rand(11, 14, 5))
outputs = lstm2d.sequence_to_images(inputs, 2)
tf.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (2, 7, 11, 5))
def testImagesAndSequenceDims(self):
with self.test_session():
size = (2, 7, 11, 5)
inputs = tf.constant(_rand(*size))
sequence = lstm2d.images_to_sequence(inputs)
outputs = lstm2d.sequence_to_images(sequence, size[0])
tf.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), size)
def testSeparableLstmDims(self):
with self.test_session():
inputs = tf.constant(_rand(2, 7, 11, 5))
outputs = lstm2d.separable_lstm(inputs, 8)
tf.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (2, 7, 11, 8))
def testReduceToSequenceDims(self):
with self.test_session():
inputs = tf.constant(_rand(2, 7, 11, 5))
outputs = lstm2d.reduce_to_sequence(inputs, 8)
tf.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (2, 11, 8))
def testReduceToFinalDims(self):
with self.test_session():
inputs = tf.constant(_rand(2, 7, 11, 5))
outputs = lstm2d.reduce_to_final(inputs, 8, 12)
tf.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (2, 8))
if __name__ == "__main__":
tf.test.main()