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extract_features.cpp
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// Copyright 2014 BVLC and contributors.
#include <stdio.h> // for snprintf
#include <cuda_runtime.h>
#include <google/protobuf/text_format.h>
#include <leveldb/db.h>
#include <leveldb/write_batch.h>
#include <boost/algorithm/string.hpp>
#include <string>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/net.hpp"
#include "caffe/vision_layers.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/io.hpp"
using namespace caffe; // NOLINT(build/namespaces)
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 = 6;
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_leveldb_name1[,name2,...] num_mini_batches [CPU/GPU]"
" [DEVICE_ID=0]\n"
"Note: you can extract multiple features in one pass by specifying"
" multiple feature blob names and leveldb names seperated by ','."
" The names cannot contain white space characters and the number of blobs"
" and leveldbs 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);
}
Caffe::set_phase(Caffe::TEST);
arg_pos = 0; // the name of the executable
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"
}
*/
string feature_extraction_proto(argv[++arg_pos]);
shared_ptr<Net<Dtype> > feature_extraction_net(
new Net<Dtype>(feature_extraction_proto));
feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
string extract_feature_blob_names(argv[++arg_pos]);
vector<string> blob_names;
boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));
string save_feature_leveldb_names(argv[++arg_pos]);
vector<string> leveldb_names;
boost::split(leveldb_names, save_feature_leveldb_names,
boost::is_any_of(","));
CHECK_EQ(blob_names.size(), leveldb_names.size()) <<
" the number of blob names and leveldb 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;
}
leveldb::Options options;
options.error_if_exists = true;
options.create_if_missing = true;
options.write_buffer_size = 268435456;
vector<shared_ptr<leveldb::DB> > feature_dbs;
for (size_t i = 0; i < num_features; ++i) {
LOG(INFO)<< "Opening leveldb " << leveldb_names[i];
leveldb::DB* db;
leveldb::Status status = leveldb::DB::Open(options,
leveldb_names[i].c_str(),
&db);
CHECK(status.ok()) << "Failed to open leveldb " << leveldb_names[i];
feature_dbs.push_back(shared_ptr<leveldb::DB>(db));
}
int num_mini_batches = atoi(argv[++arg_pos]);
LOG(ERROR)<< "Extacting Features";
Datum datum;
vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
num_features,
shared_ptr<leveldb::WriteBatch>(new leveldb::WriteBatch()));
const int kMaxKeyStrLength = 100;
char key_str[kMaxKeyStrLength];
vector<Blob<float>*> input_vec;
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;
Dtype* feature_blob_data;
for (int n = 0; n < batch_size; ++n) {
datum.set_height(dim_features);
datum.set_width(1);
datum.set_channels(1);
datum.clear_data();
datum.clear_float_data();
feature_blob_data = feature_blob->mutable_cpu_data() +
feature_blob->offset(n);
for (int d = 0; d < dim_features; ++d) {
datum.add_float_data(feature_blob_data[d]);
}
string value;
datum.SerializeToString(&value);
snprintf(key_str, kMaxKeyStrLength, "%d", image_indices[i]);
feature_batches[i]->Put(string(key_str), value);
++image_indices[i];
if (image_indices[i] % 1000 == 0) {
feature_dbs[i]->Write(leveldb::WriteOptions(),
feature_batches[i].get());
LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
" query images for feature blob " << blob_names[i];
feature_batches[i].reset(new leveldb::WriteBatch());
}
} // 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) {
feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i].get());
}
LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
" query images for feature blob " << blob_names[i];
}
LOG(ERROR)<< "Successfully extracted the features!";
return 0;
}