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
|
"""Tests for tensorflow.ops.reverse_sequence_op."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker as gc
class ReverseSequenceTest(tf.test.TestCase):
def _testReverseSequence(self, x, seq_dim, seq_lengths,
truth, use_gpu=False, expected_err_re=None):
with self.test_session(use_gpu=use_gpu):
ans = tf.reverse_sequence(x,
seq_dim=seq_dim,
seq_lengths=seq_lengths)
if expected_err_re is None:
tf_ans = ans.eval()
self.assertAllClose(tf_ans, truth, atol=1e-10)
self.assertShapeEqual(truth, ans)
else:
with self.assertRaisesOpError(expected_err_re):
ans.eval()
def _testBothReverseSequence(self, x, seq_dim, seq_lengths,
truth, expected_err_re=None):
self._testReverseSequence(x, seq_dim, seq_lengths,
truth, True, expected_err_re)
self._testReverseSequence(x, seq_dim, seq_lengths,
truth, False, expected_err_re)
def _testBasic(self, dtype):
x = np.asarray([
[[1, 2, 3, 4], [5, 6, 7, 8]],
[[9, 10, 11, 12], [13, 14, 15, 16]],
[[17, 18, 19, 20], [21, 22, 23, 24]]], dtype=dtype)
x = x.reshape(3, 2, 4, 1, 1)
# reverse dim 2 up to (0:3, none, 0:4) along dim=0
seq_dim = 2
seq_lengths = np.asarray([3, 0, 4], dtype=np.int64)
truth = np.asarray(
[[[3, 2, 1, 4], [7, 6, 5, 8]], # reverse 0:3
[[9, 10, 11, 12], [13, 14, 15, 16]], # reverse none
[[20, 19, 18, 17], [24, 23, 22, 21]]], # reverse 0:4 (all)
dtype=dtype)
truth = truth.reshape(3, 2, 4, 1, 1)
self._testBothReverseSequence(x, seq_dim, seq_lengths, truth)
def testFloatBasic(self):
self._testBasic(np.float32)
def testDoubleBasic(self):
self._testBasic(np.float64)
def testInt32Basic(self):
self._testBasic(np.int32)
def testInt64Basic(self):
self._testBasic(np.int64)
def testSComplexBasic(self):
self._testBasic(np.complex64)
def testFloatReverseSequenceGrad(self):
x = np.asarray([
[[1, 2, 3, 4], [5, 6, 7, 8]],
[[9, 10, 11, 12], [13, 14, 15, 16]],
[[17, 18, 19, 20], [21, 22, 23, 24]]], dtype=np.float)
x = x.reshape(3, 2, 4, 1, 1)
# reverse dim 2 up to (0:3, none, 0:4) along dim=0
seq_dim = 2
seq_lengths = np.asarray([3, 0, 4], dtype=np.int64)
with self.test_session():
input_t = tf.constant(x, shape=x.shape)
seq_lengths_t = tf.constant(seq_lengths, shape=seq_lengths.shape)
reverse_sequence_out = tf.reverse_sequence(input_t,
seq_dim=seq_dim,
seq_lengths=seq_lengths_t)
err = gc.ComputeGradientError(input_t,
x.shape,
reverse_sequence_out,
x.shape,
x_init_value=x)
print "ReverseSequence gradient error = %g" % err
self.assertLess(err, 1e-8)
def testShapeFunctionEdgeCases(self):
# Batch size mismatched between input and seq_lengths.
with self.assertRaises(ValueError):
tf.reverse_sequence(
tf.placeholder(tf.float32, shape=(32, 2, 3)),
seq_lengths=tf.placeholder(tf.int64, shape=(33,)),
seq_dim=3)
# seq_dim out of bounds.
with self.assertRaisesRegexp(ValueError, "seq_dim must be < input.dims()"):
tf.reverse_sequence(
tf.placeholder(tf.float32, shape=(32, 2, 3)),
seq_lengths=tf.placeholder(tf.int64, shape=(32,)),
seq_dim=3)
if __name__ == "__main__":
tf.test.main()
|
