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
|
# 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 tensorflow.ctc_ops.ctc_loss_op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from six.moves import zip_longest
import tensorflow as tf
def grouper(iterable, n, fillvalue=None):
"""Collect data into fixed-length chunks or blocks."""
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx
args = [iter(iterable)] * n
return zip_longest(fillvalue=fillvalue, *args)
def flatten(list_of_lists):
"""Flatten one level of nesting."""
return itertools.chain.from_iterable(list_of_lists)
class CTCGreedyDecoderTest(tf.test.TestCase):
def _testCTCDecoder(self, decoder, inputs, seq_lens, log_prob_truth,
decode_truth, expected_err_re=None, **decoder_args):
inputs_t = [tf.convert_to_tensor(x) for x in inputs]
# convert inputs_t into a [max_time x batch_size x depth] tensor
# from a len time python list of [batch_size x depth] tensors
inputs_t = tf.stack(inputs_t)
with self.test_session(use_gpu=False) as sess:
decoded_list, log_probability = decoder(
inputs_t,
sequence_length=seq_lens, **decoder_args)
decoded_unwrapped = list(flatten([
(st.indices, st.values, st.dense_shape) for st in decoded_list]))
if expected_err_re is None:
outputs = sess.run(
decoded_unwrapped + [log_probability])
# Group outputs into (ix, vals, shape) tuples
output_sparse_tensors = list(grouper(outputs[:-1], 3))
output_log_probability = outputs[-1]
# Check the number of decoded outputs (top_paths) match
self.assertEqual(len(output_sparse_tensors), len(decode_truth))
# For each SparseTensor tuple, compare (ix, vals, shape)
for out_st, truth_st, tf_st in zip(
output_sparse_tensors, decode_truth, decoded_list):
self.assertAllEqual(out_st[0], truth_st[0]) # ix
self.assertAllEqual(out_st[1], truth_st[1]) # vals
self.assertAllEqual(out_st[2], truth_st[2]) # shape
# Compare the shapes of the components with the truth. The
# `None` elements are not known statically.
self.assertEqual([None, truth_st[0].shape[1]],
tf_st.indices.get_shape().as_list())
self.assertEqual([None], tf_st.values.get_shape().as_list())
self.assertShapeEqual(truth_st[2], tf_st.shape)
# Make sure decoded probabilities match
self.assertAllClose(output_log_probability, log_prob_truth, atol=1e-6)
else:
with self.assertRaisesOpError(expected_err_re):
sess.run(decoded_unwrapped + [log_probability])
def testCTCGreedyDecoder(self):
"""Test two batch entries - best path decoder."""
max_time_steps = 6
# depth == 4
seq_len_0 = 4
input_prob_matrix_0 = np.asarray(
[[1.0, 0.0, 0.0, 0.0], # t=0
[0.0, 0.0, 0.4, 0.6], # t=1
[0.0, 0.0, 0.4, 0.6], # t=2
[0.0, 0.9, 0.1, 0.0], # t=3
[0.0, 0.0, 0.0, 0.0], # t=4 (ignored)
[0.0, 0.0, 0.0, 0.0]], # t=5 (ignored)
dtype=np.float32)
input_log_prob_matrix_0 = np.log(input_prob_matrix_0)
seq_len_1 = 5
# dimensions are time x depth
input_prob_matrix_1 = np.asarray(
[[0.1, 0.9, 0.0, 0.0], # t=0
[0.0, 0.9, 0.1, 0.0], # t=1
[0.0, 0.0, 0.1, 0.9], # t=2
[0.0, 0.9, 0.1, 0.1], # t=3
[0.9, 0.1, 0.0, 0.0], # t=4
[0.0, 0.0, 0.0, 0.0]], # t=5 (ignored)
dtype=np.float32)
input_log_prob_matrix_1 = np.log(input_prob_matrix_1)
# len max_time_steps array of batch_size x depth matrices
inputs = [np.vstack([input_log_prob_matrix_0[t, :],
input_log_prob_matrix_1[t, :]])
for t in range(max_time_steps)]
# batch_size length vector of sequence_lengths
seq_lens = np.array([seq_len_0, seq_len_1], dtype=np.int32)
# batch_size length vector of negative log probabilities
log_prob_truth = np.array([
np.sum(-np.log([1.0, 0.6, 0.6, 0.9])),
np.sum(-np.log([0.9, 0.9, 0.9, 0.9, 0.9]))
], np.float32)[:, np.newaxis]
# decode_truth: one SparseTensor (ix, vals, shape)
decode_truth = [
(np.array([[0, 0], # batch 0, 2 outputs
[0, 1],
[1, 0], # batch 1, 3 outputs
[1, 1],
[1, 2]], dtype=np.int64),
np.array([0, 1, # batch 0
1, 1, 0], # batch 1
dtype=np.int64),
# shape is batch x max_decoded_length
np.array([2, 3], dtype=np.int64)),
]
self._testCTCDecoder(
tf.nn.ctc_greedy_decoder,
inputs, seq_lens, log_prob_truth, decode_truth)
def testCTCDecoderBeamSearch(self):
"""Test one batch, two beams - hibernating beam search."""
# max_time_steps == 8
depth = 6
seq_len_0 = 5
input_prob_matrix_0 = np.asarray(
[[0.30999, 0.309938, 0.0679938, 0.0673362, 0.0708352, 0.173908],
[0.215136, 0.439699, 0.0370931, 0.0393967, 0.0381581, 0.230517],
[0.199959, 0.489485, 0.0233221, 0.0251417, 0.0233289, 0.238763],
[0.279611, 0.452966, 0.0204795, 0.0209126, 0.0194803, 0.20655],
[0.51286, 0.288951, 0.0243026, 0.0220788, 0.0219297, 0.129878],
# Random entry added in at time=5
[0.155251, 0.164444, 0.173517, 0.176138, 0.169979, 0.160671]],
dtype=np.float32)
# Add arbitrary offset - this is fine
input_log_prob_matrix_0 = np.log(input_prob_matrix_0) + 2.0
# len max_time_steps array of batch_size x depth matrices
inputs = ([input_log_prob_matrix_0[t, :][np.newaxis, :]
for t in range(seq_len_0)] # Pad to max_time_steps = 8
+ 2 * [np.zeros((1, depth), dtype=np.float32)])
# batch_size length vector of sequence_lengths
seq_lens = np.array([seq_len_0], dtype=np.int32)
# batch_size length vector of negative log probabilities
log_prob_truth = np.array([
0.584855, # output beam 0
0.389139 # output beam 1
], np.float32)[np.newaxis, :]
# decode_truth: two SparseTensors, (ix, values, shape)
decode_truth = [
# beam 0, batch 0, two outputs decoded
(np.array([[0, 0], [0, 1]], dtype=np.int64),
np.array([1, 0], dtype=np.int64),
np.array([1, 2], dtype=np.int64)),
# beam 1, batch 0, three outputs decoded
(np.array([[0, 0], [0, 1], [0, 2]], dtype=np.int64),
np.array([0, 1, 0], dtype=np.int64),
np.array([1, 3], dtype=np.int64)),
]
self._testCTCDecoder(
tf.nn.ctc_beam_search_decoder,
inputs, seq_lens, log_prob_truth,
decode_truth,
beam_width=2,
top_paths=2)
if __name__ == "__main__":
tf.test.main()
|
