online-nnet2-decodable-test.cc
3.28 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
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
// nnet2/online-nnet2-decodable-test.cc
// Copyright 2014 Johns Hopkins University (author: Daniel Povey)
// See ../../COPYING for clarification regarding multiple authors
//
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "hmm/transition-model.h"
#include "nnet2/nnet-component.h"
#include "nnet2/decodable-am-nnet.h"
#include "nnet2/online-nnet2-decodable.h"
#include "feat/online-feature.h"
#include "hmm/hmm-test-utils.h"
namespace kaldi {
namespace nnet2 {
void UnitTestNnetDecodable() {
std::vector<int32> phones;
phones.push_back(1);
for (int32 i = 2; i < 20; i++)
if (rand() % 2 == 0)
phones.push_back(i);
int32 N = 2 + rand() % 2, // context-size N is 2 or 3.
P = rand() % N; // Central-phone is random on [0, N)
std::vector<int32> num_pdf_classes;
ContextDependency *ctx_dep =
GenRandContextDependencyLarge(phones, N, P,
true, &num_pdf_classes);
HmmTopology topo = GetDefaultTopology(phones);
TransitionModel trans_model(*ctx_dep, topo);
delete ctx_dep; // We won't need this further.
ctx_dep = NULL;
int32 input_dim = 40, output_dim = trans_model.NumPdfs();
Nnet *nnet = GenRandomNnet(input_dim, output_dim);
AmNnet am_nnet(*nnet);
delete nnet;
nnet = NULL;
Vector<BaseFloat> priors(output_dim);
priors.SetRandn();
priors.ApplyExp();
priors.Scale(1.0 / priors.Sum());
am_nnet.SetPriors(priors);
DecodableNnet2OnlineOptions opts;
opts.max_nnet_batch_size = 20;
opts.acoustic_scale = 0.1;
opts.pad_input = (rand() % 2 == 0);
int32 num_input_frames = 400;
Matrix<BaseFloat> input_feats(num_input_frames, input_dim);
input_feats.SetRandn();
OnlineMatrixFeature matrix_feature(input_feats);
DecodableNnet2Online online_decodable(am_nnet, trans_model,
opts, &matrix_feature);
DecodableAmNnet offline_decodable(trans_model, am_nnet,
CuMatrix<BaseFloat>(input_feats),
opts.pad_input,
opts.acoustic_scale);
KALDI_ASSERT(online_decodable.NumFramesReady() ==
offline_decodable.NumFramesReady());
int32 num_frames = online_decodable.NumFramesReady(),
num_tids = trans_model.NumTransitionIds();
for (int32 i = 0; i < 50; i++) {
int32 t = rand() % num_frames, tid = 1 + rand() % num_tids;
BaseFloat l1 = online_decodable.LogLikelihood(t, tid),
l2 = offline_decodable.LogLikelihood(t, tid);
KALDI_ASSERT(ApproxEqual(l1, l2));
}
}
} // namespace nnet2
} // namespace kaldi
int main() {
using namespace kaldi;
using namespace kaldi::nnet2;
using kaldi::int32;
for (int32 i = 0; i < 3; i++)
UnitTestNnetDecodable();
return 0;
}