#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[]);
const int num_required_args = ;
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 ;
}
int arg_pos = num_required_args;
arg_pos = num_required_args;
if (argc > arg_pos && strcmp(argv[arg_pos], "GPU") == ) {
LOG(ERROR)<< "Using GPU";
uint device_id = ;
if (argc > arg_pos + ) {
device_id = atoi(argv[arg_pos + ]);
CHECK_GE(device_id, );
}
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 = ; // 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 = ; 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 = ; 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 = ;
char key_str[kMaxKeyStrLength];
std::vector<Blob<float>*> input_vec;
std::vector<int> image_indices(num_features, );
for (int batch_index = ; batch_index < num_mini_batches; ++batch_index) {
feature_extraction_net->Forward(input_vec);
for (int i = ; 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 = ; 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 = ; 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] % == ) {
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 = ; i < num_features; ++i) {
if (image_indices[i] % != ) {
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 ;
}
