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
|
#include <stdio.h> // for snprintf
#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/io.hpp"
#include "caffe/vision_layers.hpp"
using caffe::Blob;
using caffe::Caffe;
using caffe::Datum;
using caffe::Net;
using boost::shared_ptr;
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";
uint 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]);
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<shared_ptr<db::DB> > feature_dbs;
std::vector<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];
shared_ptr<db::DB> db(db::GetDB(db_type));
db->Open(dataset_names.at(i), db::NEW);
feature_dbs.push_back(db);
shared_ptr<db::Transaction> txn(db->NewTransaction());
txns.push_back(txn);
}
LOG(ERROR)<< "Extacting Features";
Datum datum;
const int kMaxKeyStrLength = 100;
char key_str[kMaxKeyStrLength];
std::vector<Blob<float>*> input_vec;
std::vector<int> image_indices(num_features, 0);
for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
feature_extraction_net->Forward(input_vec);
for (int i = 0; i < num_features; ++i) {
const 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]);
}
int length = snprintf(key_str, kMaxKeyStrLength, "%010d",
image_indices[i]);
string out;
CHECK(datum.SerializeToString(&out));
txns.at(i)->Put(std::string(key_str, length), 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;
}
|
