FazBrowse GitHub Viewer
|
Trending
|
URL:
|
Home
Tools:
[Download Repo ZIP]
[View Raw Code]
[Original HTTPS Page]
Caffe-HRT/tools/extract_features.cpp at master · farisais/Caffe-HRT · GitHub
farisais
Caffe-HRT
Repository navigation
Code
Pull requests
Actions
Projects
Wiki
Security and quality
Insights
Expand file tree
Breadcrumbs
Caffe-HRT
/
tools
/
extract_features.cpp
Copy path
More file actions
More file actions
Latest commit
History
History
History
183 lines (165 loc) · 6.23 KB
Breadcrumbs
Caffe-HRT
/
tools
/
extract_features.cpp
Copy path
File metadata and controls
183 lines (165 loc) · 6.23 KB
Raw
Copy raw file
Download raw file
Open symbols panel
Edit and raw actions
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
#
include
<
string
>
#
include
<
vector
>
#
include
"
boost/algorithm/string.hpp
"
#
include
"
google/protobuf/text_format.h
"
#
include
"
caffe/blob.hpp
"
#
include
"
caffe/common.hpp
"
#
include
"
caffe/net.hpp
"
#
include
"
caffe/proto/caffe.pb.h
"
#
include
"
caffe/util/db.hpp
"
#
include
"
caffe/util/format.hpp
"
#
include
"
caffe/util/io.hpp
"
using
caffe::Blob;
using
caffe::Caffe;
using
caffe::Datum;
using
caffe::Net;
using
std::string;
namespace
db
=
caffe::db;
template
<
typename
Dtype>
int
feature_extraction_pipeline
(
int
argc,
char
** argv);
int
main
(
int
argc,
char
** argv) {
return
feature_extraction_pipeline<
float
>(argc, argv);
//
return feature_extraction_pipeline<double>(argc, argv);
}
template
<
typename
Dtype>
int
feature_extraction_pipeline
(
int
argc,
char
** argv) {
::google::InitGoogleLogging
(argv[
0
]);
const
int
num_required_args =
7
;
if
(argc < num_required_args) {
LOG
(
ERROR
)<<
"
This program takes in a trained network and an input data layer, and then
"
"
extract features of the input data produced by the net.
\n
"
"
Usage: extract_features pretrained_net_param
"
"
feature_extraction_proto_file extract_feature_blob_name1[,name2,...]
"
"
save_feature_dataset_name1[,name2,...] num_mini_batches db_type
"
"
[CPU/GPU] [DEVICE_ID=0]
\n
"
"
Note: you can extract multiple features in one pass by specifying
"
"
multiple feature blob names and dataset names separated by ','.
"
"
The names cannot contain white space characters and the number of blobs
"
"
and datasets must be equal.
"
;
return
1
;
}
int
arg_pos = num_required_args;
arg_pos = num_required_args;
if
(argc > arg_pos &&
strcmp
(argv[arg_pos],
"
GPU
"
) ==
0
) {
LOG
(
ERROR
)<<
"
Using GPU
"
;
int
device_id =
0
;
if
(argc > arg_pos +
1
) {
device_id =
atoi
(argv[arg_pos +
1
]);
CHECK_GE
(device_id,
0
);
}
LOG
(
ERROR
) <<
"
Using Device_id=
"
<< device_id;
Caffe::SetDevice
(device_id);
Caffe::set_mode
(Caffe::
GPU
);
}
else
{
LOG
(
ERROR
) <<
"
Using CPU
"
;
Caffe::set_mode
(Caffe::
CPU
);
}
arg_pos =
0
;
//
the name of the executable
std::string
pretrained_binary_proto
(argv[++arg_pos]);
//
Expected prototxt contains at least one data layer such as
//
the layer data_layer_name and one feature blob such as the
//
fc7 top blob to extract features.
/*
layers {
name: "data_layer_name"
type: DATA
data_param {
source: "/path/to/your/images/to/extract/feature/images_leveldb"
mean_file: "/path/to/your/image_mean.binaryproto"
batch_size: 128
crop_size: 227
mirror: false
}
top: "data_blob_name"
top: "label_blob_name"
}
layers {
name: "drop7"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
bottom: "fc7"
top: "fc7"
}
*/
std::string
feature_extraction_proto
(argv[++arg_pos]);
boost::shared_ptr<Net<Dtype> >
feature_extraction_net
(
new
Net<Dtype>(feature_extraction_proto, caffe::
TEST
));
feature_extraction_net->
CopyTrainedLayersFrom
(pretrained_binary_proto);
std::string
extract_feature_blob_names
(argv[++arg_pos]);
std::vector<std::string> blob_names;
boost::split
(blob_names, extract_feature_blob_names,
boost::is_any_of
(
"
,
"
));
std::string
save_feature_dataset_names
(argv[++arg_pos]);
std::vector<std::string> dataset_names;
boost::split
(dataset_names, save_feature_dataset_names,
boost::is_any_of
(
"
,
"
));
CHECK_EQ
(blob_names.
size
(), dataset_names.
size
()) <<
"
the number of blob names and dataset names must be equal
"
;
size_t
num_features = blob_names.
size
();
for
(
size_t
i =
0
; i < num_features; i++) {
CHECK
(feature_extraction_net->
has_blob
(blob_names[i]))
<<
"
Unknown feature blob name
"
<< blob_names[i]
<<
"
in the network
"
<< feature_extraction_proto;
}
int
num_mini_batches =
atoi
(argv[++arg_pos]);
std::vector<boost::shared_ptr<db::
DB
> > feature_dbs;
std::vector<boost::shared_ptr<db::Transaction> > txns;
const
char
* db_type = argv[++arg_pos];
for
(
size_t
i =
0
; i < num_features; ++i) {
LOG
(
INFO
)<<
"
Opening dataset
"
<< dataset_names[i];
boost::shared_ptr<db::
DB
>
db
(
db::GetDB
(db_type));
db->
Open
(dataset_names.
at
(i), db::
NEW
);
feature_dbs.
push_back
(db);
boost::shared_ptr<db::Transaction>
txn
(db->
NewTransaction
());
txns.
push_back
(txn);
}
LOG
(
ERROR
)<<
"
Extracting Features
"
;
Datum datum;
std::vector<
int
>
image_indices
(num_features,
0
);
for
(
int
batch_index =
0
; batch_index < num_mini_batches; ++batch_index) {
feature_extraction_net->
Forward
();
for
(
int
i =
0
; i < num_features; ++i) {
const
boost::shared_ptr<Blob<Dtype> > feature_blob =
feature_extraction_net->
blob_by_name
(blob_names[i]);
int
batch_size = feature_blob->
num
();
int
dim_features = feature_blob->
count
() / batch_size;
const
Dtype* feature_blob_data;
for
(
int
n =
0
; n < batch_size; ++n) {
datum.
set_height
(feature_blob->
height
());
datum.
set_width
(feature_blob->
width
());
datum.
set_channels
(feature_blob->
channels
());
datum.
clear_data
();
datum.
clear_float_data
();
feature_blob_data = feature_blob->
cpu_data
() +
feature_blob->
offset
(n);
for
(
int
d =
0
; d < dim_features; ++d) {
datum.
add_float_data
(feature_blob_data[d]);
}
string key_str =
caffe::format_int
(image_indices[i],
10
);
string out;
CHECK
(datum.
SerializeToString
(&out));
txns.
at
(i)->
Put
(key_str, out);
++image_indices[i];
if
(image_indices[i] %
1000
==
0
) {
txns.
at
(i)->
Commit
();
txns.
at
(i).
reset
(feature_dbs.
at
(i)->
NewTransaction
());
LOG
(
ERROR
)<<
"
Extracted features of
"
<< image_indices[i] <<
"
query images for feature blob
"
<< blob_names[i];
}
}
//
for (int n = 0; n < batch_size; ++n)
}
//
for (int i = 0; i < num_features; ++i)
}
//
for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
//
write the last batch
for
(
int
i =
0
; i < num_features; ++i) {
if
(image_indices[i] %
1000
!=
0
) {
txns.
at
(i)->
Commit
();
}
LOG
(
ERROR
)<<
"
Extracted features of
"
<< image_indices[i] <<
"
query images for feature blob
"
<< blob_names[i];
feature_dbs.
at
(i)->
Close
();
}
LOG
(
ERROR
)<<
"
Successfully extracted the features!
"
;
return
0
;
}
Back
|
FazBrowse Home
|
New Git URL