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
|
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
class TestCTCGreedyDecoderOp(serial.SerializedTestCase):
@given(
batch=st.sampled_from([2, 4, 128, 256]),
max_time=st.sampled_from([2, 10, 30, 50]),
num_classes=st.sampled_from([2, 10, 26, 40]),
merge_repeated=st.sampled_from([True, False]),
**hu.gcs_cpu_only
)
@settings(deadline=10000)
def test_ctc_greedy_decoder(
self, batch, max_time,
num_classes, merge_repeated, gc, dc
):
def input_generater():
inputs = np.random.rand(max_time, batch, num_classes)\
.astype(np.float32)
seq_len = np.random.randint(1, max_time + 1, size=batch)\
.astype(np.int32)
return inputs, seq_len
def ref_ctc_decoder(inputs, seq_len):
merge = merge_repeated
output_len = np.array([]).astype(np.int32)
val = np.array([]).astype(np.int32)
for i in range(batch):
prev_id = 0
t_dec = 0
len_i = seq_len[i] if seq_len is not None else max_time
for t in range(len_i):
max_id = np.argmax(inputs[t, i, :])
if max_id == 0:
prev_id = max_id
continue
if max_id == prev_id and merge:
prev_id = max_id
continue
t_dec += 1
val = np.append(val, max_id)
prev_id = max_id
output_len = np.append(output_len, t_dec)
return [output_len, val]
def ref_ctc_decoder_max_time(inputs):
return ref_ctc_decoder(inputs, None)
inputs, seq_len = input_generater()
op = core.CreateOperator('CTCGreedyDecoder',
['INPUTS', 'SEQ_LEN'],
['OUTPUT_LEN', 'VALUES'],
merge_repeated=merge_repeated)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[inputs, seq_len],
reference=ref_ctc_decoder,
)
op_1 = core.CreateOperator('CTCGreedyDecoder',
['INPUTS'],
['OUTPUT_LEN', 'VALUES'],
merge_repeated=merge_repeated)
self.assertReferenceChecks(
device_option=gc,
op=op_1,
inputs=[inputs],
reference=ref_ctc_decoder_max_time,
)
@given(
batch=st.sampled_from([2, 4, 128, 256]),
max_time=st.sampled_from([2, 10, 30, 50]),
num_classes=st.sampled_from([2, 10, 26, 40]),
**hu.gcs_cpu_only
)
@settings(deadline=10000)
def test_ctc_greedy_decoder_no_merge_arg(
self, batch, max_time,
num_classes, gc, dc
):
def input_generater():
inputs = np.random.rand(max_time, batch, num_classes)\
.astype(np.float32)
seq_len = np.random.randint(1, max_time + 1, size=batch)\
.astype(np.int32)
return inputs, seq_len
def ref_ctc_decoder_no_merge_arg(inputs, seq_len):
merge = True
output_len = np.array([]).astype(np.int32)
val = np.array([]).astype(np.int32)
for i in range(batch):
prev_id = 0
t_dec = 0
len_i = seq_len[i] if seq_len is not None else max_time
for t in range(len_i):
max_id = np.argmax(inputs[t, i, :])
if max_id == 0:
prev_id = max_id
continue
if max_id == prev_id and merge:
prev_id = max_id
continue
t_dec += 1
val = np.append(val, max_id)
prev_id = max_id
output_len = np.append(output_len, t_dec)
return [output_len, val]
def ref_ctc_decoder_max_time(inputs):
return ref_ctc_decoder_no_merge_arg(inputs, None)
inputs, seq_len = input_generater()
op = core.CreateOperator('CTCGreedyDecoder',
['INPUTS'],
['OUTPUT_LEN', 'VALUES'])
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[inputs],
reference=ref_ctc_decoder_max_time,
)
if __name__ == "__main__":
import random
random.seed(2603)
unittest.main()
|
