int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
if (argc != 3) {
LOG(ERROR)<< "Usage: demo_compute_image_mean input_leveldb output_file";
return(0);
}
leveldb::DB* db;
leveldb::Options options;
options.create_if_missing = false;
LOG(INFO) << "Opening leveldb " << argv[1];
leveldb::Status status = leveldb::DB::Open(options, argv[1], &db);
CHECK(status.ok()) << "Failed to open leveldb " << argv[1];
leveldb::ReadOptions read_options;
read_options.fill_cache = false;
leveldb::Iterator* it = db->NewIterator(read_options);
it->SeekToFirst();
Datum datum;
BlobProto sum_blob;
int count = 0;
datum.ParseFromString(it->value().ToString());
sum_blob.set_num(1);
sum_blob.set_channels(datum.channels());
sum_blob.set_height(datum.height());
sum_blob.set_width(datum.width());
const int data_size = datum.channels() * datum.height() * datum.width();
for (int i = 0; i < datum.data().size(); ++i) {
sum_blob.add_data(0.);
}
LOG(INFO) << "Starting Iteration";
for (it->SeekToFirst(); it->Valid(); it->Next()) {
// just a dummy operation
datum.ParseFromString(it->value().ToString());
const string& data = datum.data();
CHECK_EQ(data.size(), data_size)<< "Incorrect data field size " << data.size();
for (int i = 0; i < data.size(); ++i) {
sum_blob.set_data(i, sum_blob.data(i) + (uint8_t) data[i]);
}
++count;
if (count % 10000 == 0) {
LOG(ERROR)<< "Processed " << count << " files.";
if (count == 100000) break;
}
}
for (int i = 0; i < sum_blob.data_size(); ++i) {
sum_blob.set_data(i, sum_blob.data(i) / count);
}
// Write to disk
LOG(INFO) << "Write to " << argv[2];
WriteProtoToBinaryFile(sum_blob, argv[2]);
delete db;
return 0;
}
cv::Mat DatumToCVMat(const Datum& datum) {
if (datum.encoded()) {
cv::Mat cv_img;
cv_img = DecodeDatumToCVMatNative(datum);
return cv_img;
}
const string& data = datum.data();
int datum_channels = datum.channels();
int datum_height = datum.height();
int datum_width = datum.width();
CHECK(datum_channels==3);
cv::Mat cv_img(datum_height, datum_width, CV_8UC3);
for (int h = 0; h < datum_height; ++h) {
for (int w = 0; w < datum_width; ++w) {
for (int c = 0; c < datum_channels; ++c) {
int datum_index = (c * datum_height + h) * datum_width + w;
cv_img.at<cv::Vec3b>(h, w)[c] = static_cast<uchar>(data[datum_index]);
}
}
}
return cv_img;
}
TEST_F(IOTest, TestReadFileToDatum) {
string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
Datum datum;
EXPECT_TRUE(ReadFileToDatum(filename, &datum));
EXPECT_TRUE(datum.encoded());
EXPECT_EQ(datum.label(), -1);
EXPECT_EQ(datum.data().size(), 140391);
}
TEST_F(IOTest, TestCVMatToDatumReference) {
string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
cv::Mat cv_img = ReadImageToCVMat(filename);
Datum datum;
CVMatToDatum(cv_img, &datum);
Datum datum_ref;
ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
EXPECT_EQ(datum.channels(), datum_ref.channels());
EXPECT_EQ(datum.height(), datum_ref.height());
EXPECT_EQ(datum.width(), datum_ref.width());
EXPECT_EQ(datum.data().size(), datum_ref.data().size());
const string& data = datum.data();
const string& data_ref = datum_ref.data();
for (int i = 0; i < datum.data().size(); ++i) {
EXPECT_TRUE(data[i] == data_ref[i]);
}
}
TEST_F(IOTest, TestDecodeDatumNativeGray) {
string filename = EXAMPLES_SOURCE_DIR "images/cat_gray.jpg";
Datum datum;
EXPECT_TRUE(ReadFileToDatum(filename, &datum));
EXPECT_TRUE(DecodeDatumNative(&datum));
EXPECT_FALSE(DecodeDatumNative(&datum));
Datum datum_ref;
ReadImageToDatumReference(filename, 0, 0, 0, false, &datum_ref);
EXPECT_EQ(datum.channels(), datum_ref.channels());
EXPECT_EQ(datum.height(), datum_ref.height());
EXPECT_EQ(datum.width(), datum_ref.width());
EXPECT_EQ(datum.data().size(), datum_ref.data().size());
const string& data = datum.data();
const string& data_ref = datum_ref.data();
for (int i = 0; i < datum.data().size(); ++i) {
EXPECT_TRUE(data[i] == data_ref[i]);
}
}
cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
cv::Mat cv_img;
CHECK(datum.encoded()) << "Datum not encoded";
const string& data = datum.data();
std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
cv_img = cv::imdecode(vec_data, -1);
if (!cv_img.data) {
LOG(ERROR) << "Could not decode datum ";
}
return cv_img;
}
vector<double> GetChannelMean(scoped_ptr<db::Cursor>& cursor)
{
vector<double> meanv(3, 0);
int count = 0;
LOG(INFO) << "Starting Iteration";
while (cursor->valid()) {
Datum datum;
datum.ParseFromString(cursor->value());
DecodeDatumNative(&datum);
const std::string& data = datum.data();
int w = datum.width(), h = datum.height();
int ch = datum.channels();
int dim = w*h;
double chmean[3] = { 0,0,0 };
for (int i = 0; i < ch;i++)
{
int chstart = i*dim;
for (int j = 0; j < dim;j++)
chmean[i] += (uint8_t)data[chstart+j];
chmean[i] /= dim;
}
if (ch == 1)
{
meanv[0] += chmean[0];
meanv[1] += chmean[0];
meanv[2] += chmean[0];
}
else
{
meanv[0] += chmean[0];
meanv[1] += chmean[1];
meanv[2] += chmean[2];
}
++count;
if (count % 10000 == 0) {
LOG(INFO) << "Processed " << count << " files.";
}
cursor->Next();
}
if (count % 10000 != 0) {
LOG(INFO) << "Processed " << count << " files.";
}
for (int c = 0; c < 3; ++c) {
LOG(INFO) << "mean_value channel [" << c << "]:" << meanv[c] / count;
}
return meanv;
}
cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color) {
cv::Mat cv_img;
CHECK(datum.encoded()) << "Datum not encoded";
const string& data = datum.data();
std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
CV_LOAD_IMAGE_GRAYSCALE);
cv_img = cv::imdecode(vec_data, cv_read_flag);
if (!cv_img.data) {
LOG(ERROR) << "Could not decode datum ";
}
return cv_img;
}
bool MostCV::LevelDBReader::GetNextEntry(string &key, vector<double> &retVec, int &label) {
if (!database_iter_->Valid())
return false;
Datum datum;
datum.clear_float_data();
datum.clear_data();
datum.ParseFromString(database_iter_->value().ToString());
key = database_iter_->key().ToString();
label = datum.label();
int expected_data_size = std::max<int>(datum.data().size(), datum.float_data_size());
const int datum_volume_size = datum.channels() * datum.height() * datum.width();
if (expected_data_size != datum_volume_size) {
cout << "Something wrong in saved data.";
assert(false);
}
retVec.resize(datum_volume_size);
const string& data = datum.data();
if (data.size() != 0) {
// Data stored in string, e.g. just pixel values of 196608 = 256 * 256 * 3
for (int i = 0; i < datum_volume_size; ++i)
retVec[i] = data[i];
} else {
// Data stored in real feature vector such as 4096 from feature extraction
for (int i = 0; i < datum_volume_size; ++i)
retVec[i] = datum.float_data(i);
}
database_iter_->Next();
++record_idx_;
return true;
}
TEST_F(IOTest, TestReadImageToDatumContentGray) {
string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
Datum datum;
const bool is_color = false;
ReadImageToDatum(filename, 0, is_color, &datum);
cv::Mat cv_img = ReadImageToCVMat(filename, is_color);
EXPECT_EQ(datum.channels(), cv_img.channels());
EXPECT_EQ(datum.height(), cv_img.rows);
EXPECT_EQ(datum.width(), cv_img.cols);
const string& data = datum.data();
int index = 0;
for (int h = 0; h < datum.height(); ++h) {
for (int w = 0; w < datum.width(); ++w) {
EXPECT_TRUE(data[index++] == static_cast<char>(cv_img.at<uchar>(h, w)));
}
}
}
cv::Mat DecodeDatumToCVMat(const Datum& datum,
const int height, const int width, const bool is_color) {
cv::Mat cv_img;
CHECK(datum.encoded()) << "Datum not encoded";
int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
CV_LOAD_IMAGE_GRAYSCALE);
const string& data = datum.data();
std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
if (height > 0 && width > 0) {
cv::Mat cv_img_origin = cv::imdecode(cv::Mat(vec_data), cv_read_flag);
cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
} else {
cv_img = cv::imdecode(vec_data, cv_read_flag);
}
if (!cv_img.data) {
LOG(ERROR) << "Could not decode datum ";
}
return cv_img;
}
TEST_F(IOTest, TestReadImageToDatumContent) {
string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
Datum datum;
ReadImageToDatum(filename, 0, &datum);
cv::Mat cv_img = ReadImageToCVMat(filename);
EXPECT_EQ(datum.channels(), cv_img.channels());
EXPECT_EQ(datum.height(), cv_img.rows);
EXPECT_EQ(datum.width(), cv_img.cols);
const string& data = datum.data();
int_tp index = 0;
for (int_tp c = 0; c < datum.channels(); ++c) {
for (int_tp h = 0; h < datum.height(); ++h) {
for (int_tp w = 0; w < datum.width(); ++w) {
EXPECT_TRUE(data[index++] ==
static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
}
}
}
}
void DataReader::Body::read_one(db::Cursor* cursor, db::Transaction* dblt, QueuePair* qp) {
Datum* datum = qp->free_.pop();
// TODO deserialize in-place instead of copy?
datum->ParseFromString(cursor->value());
if (dblt != NULL) {
string labels;
CHECK_EQ(dblt->Get(cursor->key(), labels), 0);
Datum labelDatum;
labelDatum.ParseFromString(labels);
// datum->MergeFrom(labelDatum);
datum->set_channels(datum->channels() + labelDatum.channels());
datum->mutable_float_data()->MergeFrom(labelDatum.float_data());
datum->mutable_data()->append(labelDatum.data());
}
qp->full_.push(datum);
// go to the next iter
cursor->Next();
if (!cursor->valid()) {
DLOG(INFO) << "Restarting data prefetching from start.";
cursor->SeekToFirst();
}
}
void DataTransformer<Dtype>::PostTransform(const int batch_item_id,
const Datum& datum,
const Dtype* mean,
Dtype* transformed_data)
{
const string& data = datum.data();
const int channels = datum.channels();
const int height = datum.height();
const int width = datum.width();
const int size = datum.channels() * datum.height() * datum.width();
/**
* only works for uint8 data data.
* post transfrom parameters:
* int : post_random_translation_size
* string : post_ground_truth_pooling_param : [num_of_pooling] [pooling_h_1] ] [pooling_w_1] [pooling_h_2],.......
* int : post_channel_for_additional_translation
*/
const int crop_size = param_.crop_size();
const bool mirror = param_.mirror();
const Dtype scale = param_.scale();
// if(param_.has_post_random_translation_size())
// {
//
// }
// if(param_.has_post_ground_truth_pooling_param())
// {
//
// }
// if(param_.has_post_channel_for_additional_translation())
// {
//
// }
}
void DataLstmTrainHistLayer<Dtype>::InternalThreadEntry() {
CPUTimer batch_timer;
batch_timer.Start();
double read_time = 0;
double trans_time = 0;
CPUTimer timer;
CHECK(this->prefetch_data_.count());
Datum datum;
Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
Dtype* top_hist = this->prefetch_hist_.mutable_cpu_data();
Dtype* top_marker = this->prefetch_marker_.mutable_cpu_data();
// datum scales
const int size = resize_height*resize_width*3;
const Dtype* mean = this->data_mean_.mutable_cpu_data();
string value;
const int kMaxKeyLength = 256;
char key_cstr[kMaxKeyLength];
int key;
const int sequence_size = this->layer_param_.data_lstm_train_hist_param().sequence_size();
const int ind_seq_num=this->layer_param_.data_lstm_train_hist_param().sequence_num();
const int interval=this->layer_param_.data_lstm_train_hist_param().interval();
int item_id;
for (int time_id = 0; time_id < sequence_size; ++time_id) {
for (int seq_id = 0; seq_id < ind_seq_num; ++seq_id) {
item_id=time_id*ind_seq_num+seq_id;
timer.Start();
// get a blob
key=buffer_key[seq_id]; // MUST be changed according to the size of the training set
snprintf(key_cstr, kMaxKeyLength, "%08d", key);
db_->Get(leveldb::ReadOptions(), string(key_cstr), &value);
datum.ParseFromString(value);
const string& data = datum.data();
read_time += timer.MicroSeconds();
timer.Start();
for (int j = 0; j < size; ++j) {
Dtype datum_element = static_cast<Dtype>(static_cast<uint8_t>(data[j]));
top_data[item_id * size + j] = (datum_element - mean[j]);
}
for (int j = 0; j < para_dim; ++j) {
top_label[item_id * para_dim + j] = datum.float_data(j);
}
top_marker[item_id] = datum.float_data(para_dim);
if (buffer_marker[seq_id] == 0) {
top_marker[item_id] = 0;
buffer_marker[seq_id] = 1;
}
//////////////////////////////////// for hist
if (top_marker[item_id] < 0.5) {
for (int j = 0; j < para_dim; ++j)
top_hist[item_id * para_dim + j] = 0;
} else {
if (time_id == 0) {
top_hist[item_id * para_dim + 0] = hist_blob[seq_id * para_dim + 0]/1.1+0.5;
top_hist[item_id * para_dim + 1] = hist_blob[seq_id * para_dim + 1]*0.17778+1.34445;
top_hist[item_id * para_dim + 2] = hist_blob[seq_id * para_dim + 2]*0.14545+0.39091;
top_hist[item_id * para_dim + 3] = hist_blob[seq_id * para_dim + 3]*0.17778-0.34445;
top_hist[item_id * para_dim + 4] = hist_blob[seq_id * para_dim + 4]/95.0+0.12;
top_hist[item_id * para_dim + 5] = hist_blob[seq_id * para_dim + 5]/95.0+0.12;
top_hist[item_id * para_dim + 6] = hist_blob[seq_id * para_dim + 6]*0.14545+1.48181;
top_hist[item_id * para_dim + 7] = hist_blob[seq_id * para_dim + 7]*0.16+0.98;
top_hist[item_id * para_dim + 8] = hist_blob[seq_id * para_dim + 8]*0.16+0.02;
top_hist[item_id * para_dim + 9] = hist_blob[seq_id * para_dim + 9]*0.14545-0.48181;
top_hist[item_id * para_dim + 10] = hist_blob[seq_id * para_dim + 10]/95.0+0.12;
top_hist[item_id * para_dim + 11] = hist_blob[seq_id * para_dim + 11]/95.0+0.12;
top_hist[item_id * para_dim + 12] = hist_blob[seq_id * para_dim + 12]/95.0+0.12;
top_hist[item_id * para_dim + 13] = hist_blob[seq_id * para_dim + 13]*0.6+0.2;
} else {
int pre_id=(time_id-1)*ind_seq_num+seq_id;
top_hist[item_id * para_dim + 0] = top_label[pre_id * para_dim + 0]/1.1+0.5;
top_hist[item_id * para_dim + 1] = top_label[pre_id * para_dim + 1]*0.17778+1.34445;
top_hist[item_id * para_dim + 2] = top_label[pre_id * para_dim + 2]*0.14545+0.39091;
top_hist[item_id * para_dim + 3] = top_label[pre_id * para_dim + 3]*0.17778-0.34445;
top_hist[item_id * para_dim + 4] = top_label[pre_id * para_dim + 4]/95.0+0.12;
top_hist[item_id * para_dim + 5] = top_label[pre_id * para_dim + 5]/95.0+0.12;
top_hist[item_id * para_dim + 6] = top_label[pre_id * para_dim + 6]*0.14545+1.48181;
top_hist[item_id * para_dim + 7] = top_label[pre_id * para_dim + 7]*0.16+0.98;
top_hist[item_id * para_dim + 8] = top_label[pre_id * para_dim + 8]*0.16+0.02;
top_hist[item_id * para_dim + 9] = top_label[pre_id * para_dim + 9]*0.14545-0.48181;
top_hist[item_id * para_dim + 10] = top_label[pre_id * para_dim + 10]/95.0+0.12;
top_hist[item_id * para_dim + 11] = top_label[pre_id * para_dim + 11]/95.0+0.12;
top_hist[item_id * para_dim + 12] = top_label[pre_id * para_dim + 12]/95.0+0.12;
top_hist[item_id * para_dim + 13] = top_label[pre_id * para_dim + 13]*0.6+0.2;
}
}
//////////////////////////////////// for hist
trans_time += timer.MicroSeconds();
buffer_key[seq_id]++;
buffer_total[seq_id]++;
if (buffer_key[seq_id]>total_frames || buffer_total[seq_id]>interval) {
buffer_key[seq_id]=random(total_frames)+1;
buffer_marker[seq_id]=0;
buffer_total[seq_id]=0;
}
//////////////////////////////////// for hist
if (time_id==sequence_size-1) {
for (int j = 0; j < para_dim; ++j)
hist_blob[seq_id * para_dim + j] = datum.float_data(j);
}
//////////////////////////////////// for hist
/*
if (seq_id == 0) {
for (int h = 0; h < resize_height; ++h) {
for (int w = 0; w < resize_width; ++w) {
leveldbTrain->imageData[(h*resize_width+w)*3+0]=(uint8_t)data[h*resize_width+w];
leveldbTrain->imageData[(h*resize_width+w)*3+1]=(uint8_t)data[resize_height*resize_width+h*resize_width+w];
leveldbTrain->imageData[(h*resize_width+w)*3+2]=(uint8_t)data[resize_height*resize_width*2+h*resize_width+w];
//leveldbTrain->imageData[(h*resize_width+w)*3+0]=(uint8_t)top_data[item_id * size+h*resize_width+w];
//leveldbTrain->imageData[(h*resize_width+w)*3+1]=(uint8_t)top_data[item_id * size+resize_height*resize_width+h*resize_width+w];
//leveldbTrain->imageData[(h*resize_width+w)*3+2]=(uint8_t)top_data[item_id * size+resize_height*resize_width*2+h*resize_width+w];
}
}
cvShowImage("Image from leveldb", leveldbTrain);
cvWaitKey( 1 );
}
*/
}
}
batch_timer.Stop();
DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
DLOG(INFO) << " Read time: " << read_time / 1000 << " ms.";
DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
}
int main(int argc, char** argv) {
#ifdef USE_OPENCV
::google::InitGoogleLogging(argv[0]);
// Print output to stderr (while still logging)
FLAGS_alsologtostderr = 1;
#ifndef GFLAGS_GFLAGS_H_
namespace gflags = google;
#endif
gflags::SetUsageMessage("Convert a set of images to the leveldb/lmdb\n"
"format used as input for Caffe.\n"
"Usage:\n"
" convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAME");
gflags::ParseCommandLineFlags(&argc, &argv, true);
if (argc < 4) {
gflags::ShowUsageWithFlagsRestrict(argv[0], "convert_imageset");
return 1;
}
const bool is_color = !FLAGS_gray;
const bool check_size = FLAGS_check_size;
const bool encoded = FLAGS_encoded;
const string encode_type = FLAGS_encode_type;
std::ifstream infile(argv[2]);
std::vector<std::pair<std::string, int> > lines;
std::string filename;
int label;
while (infile >> filename >> label) {
lines.push_back(std::make_pair(filename, label));
}
if (FLAGS_shuffle) {
// randomly shuffle data
LOG(INFO) << "Shuffling data";
shuffle(lines.begin(), lines.end());
}
LOG(INFO) << "A total of " << lines.size() << " images.";
if (encode_type.size() && !encoded)
LOG(INFO) << "encode_type specified, assuming encoded=true.";
int resize_height = std::max<int>(0, FLAGS_resize_height);
int resize_width = std::max<int>(0, FLAGS_resize_width);
// Create new DB
scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
db->Open(argv[3], db::NEW);
scoped_ptr<db::Transaction> txn(db->NewTransaction());
// Storing to db
std::string root_folder(argv[1]);
Datum datum;
int count = 0;
int data_size = 0;
bool data_size_initialized = false;
for (int line_id = 0; line_id < lines.size(); ++line_id) {
bool status;
std::string enc = encode_type;
if (encoded && !enc.size()) {
// Guess the encoding type from the file name
string fn = lines[line_id].first;
size_t p = fn.rfind('.');
if ( p == fn.npos )
LOG(WARNING) << "Failed to guess the encoding of '" << fn << "'";
enc = fn.substr(p);
std::transform(enc.begin(), enc.end(), enc.begin(), ::tolower);
}
status = ReadImageToDatum(root_folder + lines[line_id].first,
lines[line_id].second, resize_height, resize_width, is_color,
enc, &datum);
if (status == false) continue;
if (check_size) {
if (!data_size_initialized) {
data_size = datum.channels() * datum.height() * datum.width();
data_size_initialized = true;
} else {
const std::string& data = datum.data();
CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
<< data.size();
}
}
// sequential
string key_str = caffe::format_int(line_id, 8) + "_" + lines[line_id].first;
// Put in db
string out;
CHECK(datum.SerializeToString(&out));
txn->Put(key_str, out);
if (++count % 1000 == 0) {
// Commit db
txn->Commit();
txn.reset(db->NewTransaction());
LOG(INFO) << "Processed " << count << " files.";
}
}
// write the last batch
if (count % 1000 != 0) {
txn->Commit();
LOG(INFO) << "Processed " << count << " files.";
}
#else
LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
#endif // USE_OPENCV
return 0;
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
#ifndef GFLAGS_GFLAGS_H_
namespace gflags = google;
#endif
gflags::SetUsageMessage("Convert a set of images to the leveldb/lmdb\n"
"format used as input for Caffe.\n"
"Usage:\n"
" convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAME\n"
"The ImageNet dataset for the training demo is at\n"
" http://www.image-net.org/download-images\n");
gflags::ParseCommandLineFlags(&argc, &argv, true);
if (argc < 4) {
gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/convert_imageset");
return 1;
}
const bool is_color = !FLAGS_gray;
const bool check_size = FLAGS_check_size;
const bool encoded = FLAGS_encoded;
std::ifstream infile(argv[2]);
std::vector<std::pair<std::string, int> > lines;
std::string filename;
int label;
while (infile >> filename >> label) {
lines.push_back(std::make_pair(filename, label));
}
if (FLAGS_shuffle) {
// randomly shuffle data
LOG(INFO) << "Shuffling data";
shuffle(lines.begin(), lines.end());
}
LOG(INFO) << "A total of " << lines.size() << " images.";
if (encoded) {
CHECK_EQ(FLAGS_resize_height, 0) << "With encoded don't resize images";
CHECK_EQ(FLAGS_resize_width, 0) << "With encoded don't resize images";
CHECK(!check_size) << "With encoded cannot check_size";
}
int resize_height = std::max<int>(0, FLAGS_resize_height);
int resize_width = std::max<int>(0, FLAGS_resize_width);
// Create new DB
scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
db->Open(argv[3], db::NEW);
scoped_ptr<db::Transaction> txn(db->NewTransaction());
// Storing to db
std::string root_folder(argv[1]);
Datum datum;
int count = 0;
const int kMaxKeyLength = 256;
char key_cstr[kMaxKeyLength];
int data_size;
bool data_size_initialized = false;
for (int line_id = 0; line_id < lines.size(); ++line_id) {
bool status;
if (encoded) {
status = ReadFileToDatum(root_folder + lines[line_id].first,
lines[line_id].second, &datum);
} else {
status = ReadImageToDatum(root_folder + lines[line_id].first,
lines[line_id].second, resize_height, resize_width, is_color, &datum);
}
if (status == false) continue;
if (check_size) {
if (!data_size_initialized) {
data_size = datum.channels() * datum.height() * datum.width();
data_size_initialized = true;
} else {
const std::string& data = datum.data();
CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
<< data.size();
}
}
// sequential
int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
lines[line_id].first.c_str());
// Put in db
string out;
CHECK(datum.SerializeToString(&out));
txn->Put(string(key_cstr, length), out);
if (++count % 1000 == 0) {
// Commit db
txn->Commit();
txn.reset(db->NewTransaction());
LOG(ERROR) << "Processed " << count << " files.";
}
}
// write the last batch
if (count % 1000 != 0) {
txn->Commit();
LOG(ERROR) << "Processed " << count << " files.";
}
return 0;
}
std::vector<float> calc_mean(const std::string &db_fname) {
scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
db->Open(db_fname, db::READ);
scoped_ptr<db::Cursor> cursor(db->NewCursor());
BlobProto sum_blob;
int count = 0;
// load first datum
Datum datum;
datum.ParseFromString(cursor->value());
if (DecodeDatumNative(&datum)) {
LOG(INFO) << "Decoding Datum";
}
sum_blob.set_num(1);
sum_blob.set_channels(datum.channels());
sum_blob.set_height(datum.height());
sum_blob.set_width(datum.width());
const int data_size = datum.channels() * datum.height() * datum.width();
int size_in_datum = std::max<int>(datum.data().size(),
datum.float_data_size());
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.add_data(0.);
}
LOG(INFO) << "Starting Iteration";
while (cursor->valid()) {
Datum datum;
datum.ParseFromString(cursor->value());
DecodeDatumNative(&datum);
const std::string& data = datum.data();
size_in_datum = std::max<int>(datum.data().size(),
datum.float_data_size());
CHECK_EQ(size_in_datum, data_size)
<< "Incorrect data field size " << size_in_datum;
if (data.size() != 0) {
CHECK_EQ(data.size(), size_in_datum);
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
}
} else {
CHECK_EQ(datum.float_data_size(), size_in_datum);
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.set_data(i, sum_blob.data(i) +
static_cast<float>(datum.float_data(i)));
}
}
++count;
if (count % 10000 == 0) {
LOG(INFO) << "Processed " << count << " files.";
}
cursor->Next();
}
if (count % 10000 != 0) {
LOG(INFO) << "Processed " << count << " files.";
}
for (int i = 0; i < sum_blob.data_size(); ++i) {
sum_blob.set_data(i, sum_blob.data(i) / count);
}
const int channels = sum_blob.channels();
const int dim = sum_blob.height() * sum_blob.width();
std::vector<float> mean_values(channels, 0.0);
LOG(INFO) << "Number of channels: " << channels;
for (int c = 0; c < channels; ++c) {
for (int i = 0; i < dim; ++i) {
mean_values[c] += sum_blob.data(dim * c + i);
}
mean_values[c] /= dim;
LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c];
}
return mean_values;
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
if (argc < 4) {
printf("Convert a set of images to the leveldb format used\n"
"as input for Caffe.\n"
"Usage:\n"
" convert_imageset ROOTFOLDER/ LISTFILE NUM_LABELS DB_NAME"
" RANDOM_SHUFFLE_DATA[0 or 1]\n");
return 0;
}
//Arguments to our program
std::ifstream infile(argv[2]);
int numLabels = atoi(argv[3]);
// Each line is constituted of the path to the file and the vector of
// labels
std::vector<std::pair<string, std::vector<float> > > lines;
// --------
string filename;
std::vector<float> labels(numLabels);
while (infile >> filename) {
for(int l=0; l<numLabels; l++)
infile >> (labels[l]);
lines.push_back(std::make_pair(filename, labels));
/*
LOG(ERROR) << "filepath: " << lines[lines.size()-1].first;
LOG(ERROR) << "values: " << lines[lines.size()-1].second[0]
<< "," << lines[lines.size()-1].second[5]
<< "," << lines[lines.size()-1].second[8];
* */
}
if (argc == 5 && argv[5][0] == '1') {
// randomly shuffle data
LOG(ERROR) << "Shuffling data";
std::random_shuffle(lines.begin(), lines.end());
}
LOG(ERROR) << "A total of " << lines.size() << " images.";
leveldb::DB* db;
leveldb::Options options;
options.error_if_exists = true;
options.create_if_missing = true;
options.write_buffer_size = 268435456;
LOG(ERROR) << "Opening leveldb " << argv[4];
leveldb::Status status = leveldb::DB::Open(
options, argv[4], &db);
CHECK(status.ok()) << "Failed to open leveldb " << argv[4];
string root_folder(argv[1]);
Datum datum;
int count = 0;
const int maxKeyLength = 256;
char key_cstr[maxKeyLength];
leveldb::WriteBatch* batch = new leveldb::WriteBatch();
int data_size;
bool data_size_initialized = false;
for (int line_id = 0; line_id < lines.size(); ++line_id) {
if (!ReadImageWithLabelVectorToDatum(root_folder + lines[line_id].first, lines[line_id].second,
&datum)) {
continue;
};
if (!data_size_initialized) {
data_size = datum.channels() * datum.height() * datum.width();
} else {
const string& data = datum.data();
CHECK_EQ(data.size(), data_size) << "Incorrect data field size " << data.size();
}
// sequential
snprintf(key_cstr, maxKeyLength, "%08d_%s", line_id, lines[line_id].first.c_str());
string value;
// get the value
datum.SerializeToString(&value);
batch->Put(string(key_cstr), value);
if (++count % 1000 == 0) {
db->Write(leveldb::WriteOptions(), batch);
LOG(ERROR) << "Processed " << count << " files.";
delete batch;
batch = new leveldb::WriteBatch();
}
}
// write the last batch
if (count % 1000 != 0) {
db->Write(leveldb::WriteOptions(), batch);
LOG(ERROR) << "Processed " << count << " files.";
}
delete batch;
delete db;
return 0;
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
if (argc < 4 || argc > 9) {
printf("Convert a set of images to the leveldb format used\n"
"as input for Caffe.\n"
"Usage:\n"
" convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME"
" RANDOM_SHUFFLE_DATA[0 or 1] DB_BACKEND[leveldb or lmdb]"
" [resize_height] [resize_width]\n"
"The ImageNet dataset for the training demo is at\n"
" http://www.image-net.org/download-images\n");
return 1;
}
// Test whether argv[1] == "-g"
bool is_color= !(string("-g") == string(argv[1]));
int arg_offset = (is_color ? 0 : 1);
std::ifstream infile(argv[arg_offset+2]);
std::vector<std::pair<string, int> > lines;
string filename;
int label;
while (infile >> filename >> label) {
lines.push_back(std::make_pair(filename, label));
}
if (argc >= (arg_offset+5) && argv[arg_offset+4][0] == '1') {
// randomly shuffle data
LOG(INFO) << "Shuffling data";
shuffle(lines.begin(), lines.end());
}
LOG(INFO) << "A total of " << lines.size() << " images.";
string db_backend = "leveldb";
if (argc >= (arg_offset+6)) {
db_backend = string(argv[arg_offset+5]);
if (!(db_backend == "leveldb") && !(db_backend == "lmdb")) {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
}
int resize_height = 0;
int resize_width = 0;
if (argc >= (arg_offset+7)) {
resize_height = atoi(argv[arg_offset+6]);
}
if (argc >= (arg_offset+8)) {
resize_width = atoi(argv[arg_offset+7]);
}
// Open new db
// lmdb
MDB_env *mdb_env;
MDB_dbi mdb_dbi;
MDB_val mdb_key, mdb_data;
MDB_txn *mdb_txn;
// leveldb
leveldb::DB* db;
leveldb::Options options;
options.error_if_exists = true;
options.create_if_missing = true;
options.write_buffer_size = 268435456;
leveldb::WriteBatch* batch = NULL;
// Open db
if (db_backend == "leveldb") { // leveldb
LOG(INFO) << "Opening leveldb " << argv[arg_offset+3];
leveldb::Status status = leveldb::DB::Open(
options, argv[arg_offset+3], &db);
CHECK(status.ok()) << "Failed to open leveldb " << argv[arg_offset+3];
batch = new leveldb::WriteBatch();
} else if (db_backend == "lmdb") { // lmdb
LOG(INFO) << "Opening lmdb " << argv[arg_offset+3];
CHECK_EQ(mkdir(argv[arg_offset+3], 0744), 0)
<< "mkdir " << argv[arg_offset+3] << "failed";
CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS) // 1TB
<< "mdb_env_set_mapsize failed";
CHECK_EQ(mdb_env_open(mdb_env, argv[3], 0, 0664), MDB_SUCCESS)
<< "mdb_env_open failed";
CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
<< "mdb_txn_begin failed";
CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
<< "mdb_open failed";
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
// Storing to db
string root_folder(argv[arg_offset+1]);
Datum datum;
int count = 0;
const int kMaxKeyLength = 256;
char key_cstr[kMaxKeyLength];
int data_size;
bool data_size_initialized = false;
for (int line_id = 0; line_id < lines.size(); ++line_id) {
if (!ReadImageToDatum(root_folder + lines[line_id].first,
lines[line_id].second, resize_height, resize_width, is_color, &datum)) {
continue;
}
if (!data_size_initialized) {
data_size = datum.channels() * datum.height() * datum.width();
data_size_initialized = true;
} else {
const string& data = datum.data();
CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
<< data.size();
}
// sequential
snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
lines[line_id].first.c_str());
string value;
datum.SerializeToString(&value);
string keystr(key_cstr);
// Put in db
if (db_backend == "leveldb") { // leveldb
batch->Put(keystr, value);
} else if (db_backend == "lmdb") { // lmdb
mdb_data.mv_size = value.size();
mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
mdb_key.mv_size = keystr.size();
mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
<< "mdb_put failed";
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
if (++count % 1000 == 0) {
// Commit txn
if (db_backend == "leveldb") { // leveldb
db->Write(leveldb::WriteOptions(), batch);
delete batch;
batch = new leveldb::WriteBatch();
} else if (db_backend == "lmdb") { // lmdb
CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
<< "mdb_txn_commit failed";
CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
<< "mdb_txn_begin failed";
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
LOG(ERROR) << "Processed " << count << " files.";
}
}
// write the last batch
if (count % 1000 != 0) {
if (db_backend == "leveldb") { // leveldb
db->Write(leveldb::WriteOptions(), batch);
delete batch;
delete db;
} else if (db_backend == "lmdb") { // lmdb
CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
mdb_close(mdb_env, mdb_dbi);
mdb_env_close(mdb_env);
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
LOG(ERROR) << "Processed " << count << " files.";
}
return 0;
}
void calc_stddev(
const std::string &db_fname,
std::vector<float> mean_values) {
scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
db->Open(db_fname, db::READ);
scoped_ptr<db::Cursor> cursor(db->NewCursor());
// load first datum
Datum datum;
datum.ParseFromString(cursor->value());
if (DecodeDatumNative(&datum)) {
LOG(INFO) << "Decoding Datum";
}
std::vector<double> stddev_values;
for (int c = 0; c < datum.channels(); ++c) {
stddev_values.push_back(0.0);
}
int files = 0;
unsigned long count = 0;
LOG(INFO) << "Starting Iteration";
while (cursor->valid()) {
Datum datum;
datum.ParseFromString(cursor->value());
DecodeDatumNative(&datum);
const int channels = datum.channels();
const int height = datum.height();
const int width = datum.width();
const std::string& data = datum.data();
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < height; ++h) {
for (int w = 0; w < width; ++w) {
const int index = c * height * width + h * width + w;
const int pixel = static_cast<uint8_t>(data[index]);
stddev_values[c] += pow((double)pixel - mean_values[c], 2.0);
}
}
}
count += width * height;
++files;
if (count % 10000 == 0) {
LOG(INFO) << "Processed " << files << " files.";
LOG(INFO) << "count:" << count;
}
cursor->Next();
}
if (files % 10000 != 0) {
LOG(INFO) << "Processed " << files << " files.";
LOG(INFO) << "count: " << count;
}
LOG(INFO) << "Finished Iteration";
std::cout.precision(15);
LOG(INFO) << "Number of channels: " << datum.channels();
for (int c = 0; c < datum.channels(); ++c) {
stddev_values[c] /= (double)count;
stddev_values[c] = sqrt(stddev_values[c]);
LOG(INFO) << "stddev_value channel [" << c << "]:"
<< std::fixed << stddev_values[c];
}
}
void DataTransformer<Dtype>::Transform(const Datum& datum,
Dtype* transformed_data) {
const string& data = datum.data();
const int datum_channels = datum.channels();
const int datum_height = datum.height();
const int datum_width = datum.width();
const int crop_size = param_.crop_size();
const Dtype scale = param_.scale();
const bool do_mirror = param_.mirror() && Rand(2);
const bool has_mean_file = param_.has_mean_file();
const bool has_uint8 = data.size() > 0;
const bool has_mean_values = mean_values_.size() > 0;
// mask_size is defaulted to 0 in caffe/proto/caffe.proto
const int mask_size = param_.mask_size();
// mask_freq is defaulted to 1 in 3 in caffe/proto/caffe.proto
const int mask_freq = param_.mask_freq();
CHECK_GT(datum_channels, 0);
CHECK_GE(datum_height, crop_size);
CHECK_GE(datum_width, crop_size);
Dtype* mean = NULL;
if (has_mean_file) {
CHECK_EQ(datum_channels, data_mean_.channels());
CHECK_EQ(datum_height, data_mean_.height());
CHECK_EQ(datum_width, data_mean_.width());
mean = data_mean_.mutable_cpu_data();
}
if (has_mean_values) {
CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) <<
"Specify either 1 mean_value or as many as channels: " << datum_channels;
if (datum_channels > 1 && mean_values_.size() == 1) {
// Replicate the mean_value for simplicity
for (int c = 1; c < datum_channels; ++c) {
mean_values_.push_back(mean_values_[0]);
}
}
}
int height = datum_height;
int width = datum_width;
int h_off = 0;
int w_off = 0;
if (crop_size) {
height = crop_size;
width = crop_size;
// We only do random crop when we do training.
if (phase_ == TRAIN) {
h_off = Rand(datum_height - crop_size + 1);
w_off = Rand(datum_width - crop_size + 1);
} else {
h_off = (datum_height - crop_size) / 2;
w_off = (datum_width - crop_size) / 2;
}
}
// initialize masking offsets to be same as cropping offsets
// so that there is no conflict
bool masking = (phase_ == TRAIN) && (mask_size > 0) && (Rand(mask_freq) == 0);
int h_mask_start = h_off;
int w_mask_start = w_off;
if (masking) {
int h_effective = datum_height;
int w_effective = datum_width;
if (crop_size) { h_effective = w_effective = crop_size; }
CHECK_GE(h_effective, mask_size);
CHECK_GE(w_effective, mask_size);
h_mask_start += Rand(h_effective-mask_size+1);
w_mask_start += Rand(w_effective-mask_size+1);
}
int h_mask_end = h_mask_start + mask_size;
int w_mask_end = w_mask_start + mask_size;
Dtype datum_element;
int top_index, data_index;
for (int c = 0; c < datum_channels; ++c) {
for (int h = 0; h < height; ++h) {
for (int w = 0; w < width; ++w) {
data_index = (c * datum_height + h_off + h) * datum_width + w_off + w;
if (do_mirror) {
top_index = (c * height + h) * width + (width - 1 - w);
} else {
top_index = (c * height + h) * width + w;
}
if (has_uint8) {
datum_element =
static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
} else {
datum_element = datum.float_data(data_index);
}
if (has_mean_file) {
transformed_data[top_index] =
(datum_element - mean[data_index]) * scale;
} else {
if (has_mean_values) {
transformed_data[top_index] =
(datum_element - mean_values_[c]) * scale;
} else {
transformed_data[top_index] = datum_element * scale;
}
}
if (masking) {
if ((h > h_mask_start) && (w > w_mask_start) &&
(h < h_mask_end) && (w < w_mask_end)) {
transformed_data[top_index] = 0;
}
}
}
}
}
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
std::ifstream infile(argv[1]);
std::vector<std::pair<string, int> > lines;
string filename;
int label;
while (infile >> filename >> label) {
lines.push_back(std::make_pair(filename, label));
}
LOG(INFO) << "A total of " << lines.size() << " images.";
Datum datum;
BlobProto sum_blob;
int count = 0;
if (!ReadImageToDatum(lines[0].first, lines[0].second,
resize_height, resize_width, is_color, &datum)) {
return -1;
}
sum_blob.set_num(1);
sum_blob.set_channels(datum.channels());
sum_blob.set_height(datum.height());
sum_blob.set_width(datum.width());
const int data_size = datum.channels() * datum.height() * datum.width();
int size_in_datum = std::max<int>(datum.data().size(),
datum.float_data_size());
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.add_data(0.);
}
LOG(INFO) << "Starting Iteration";
for (int line_id = 0; line_id < lines.size(); ++line_id) {
if (!ReadImageToDatum(lines[line_id].first, lines[line_id].second,
resize_height, resize_width, is_color, &datum)) {
continue;
}
const string& data = datum.data();
size_in_datum = std::max<int>(datum.data().size(),
datum.float_data_size());
CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
size_in_datum;
if (data.size() != 0) {
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
}
} else {
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.set_data(i, sum_blob.data(i) +
static_cast<float>(datum.float_data(i)));
}
}
++count;
}
for (int i = 0; i < sum_blob.data_size(); ++i) {
sum_blob.set_data(i, sum_blob.data(i) / count);
}
// Write to disk
LOG(INFO) << "Write to " << argv[2];
WriteProtoToBinaryFile(sum_blob, argv[2]);
return 0;
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
if (argc < 3 || argc > 4) {
LOG(ERROR) << "Usage: compute_image_mean input_db output_file"
<< " db_backend[leveldb or lmdb]";
return 1;
}
string db_backend = "lmdb";
if (argc == 4) {
db_backend = string(argv[3]);
}
// Open leveldb
leveldb::DB* db;
leveldb::Options options;
options.create_if_missing = false;
leveldb::Iterator* it = NULL;
// lmdb
MDB_env* mdb_env;
MDB_dbi mdb_dbi;
MDB_val mdb_key, mdb_value;
MDB_txn* mdb_txn;
MDB_cursor* mdb_cursor;
// Open db
if (db_backend == "leveldb") { // leveldb
LOG(INFO) << "Opening leveldb " << argv[1];
leveldb::Status status = leveldb::DB::Open(
options, argv[1], &db);
CHECK(status.ok()) << "Failed to open leveldb " << argv[1];
leveldb::ReadOptions read_options;
read_options.fill_cache = false;
it = db->NewIterator(read_options);
it->SeekToFirst();
} else if (db_backend == "lmdb") { // lmdb
LOG(INFO) << "Opening lmdb " << argv[1];
CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS); // 1TB
CHECK_EQ(mdb_env_open(mdb_env, argv[1], MDB_RDONLY, 0664),
MDB_SUCCESS) << "mdb_env_open failed";
CHECK_EQ(mdb_txn_begin(mdb_env, NULL, MDB_RDONLY, &mdb_txn), MDB_SUCCESS)
<< "mdb_txn_begin failed";
CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
<< "mdb_open failed";
CHECK_EQ(mdb_cursor_open(mdb_txn, mdb_dbi, &mdb_cursor), MDB_SUCCESS)
<< "mdb_cursor_open failed";
CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
MDB_SUCCESS);
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
// set size info
Datum datum;
BlobProto sum_blob;
int count = 0;
// load first datum
if (db_backend == "leveldb") {
datum.ParseFromString(it->value().ToString());
} else if (db_backend == "lmdb") {
datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
sum_blob.set_num(1);
sum_blob.set_channels(datum.channels());
sum_blob.set_height(datum.height());
sum_blob.set_width(datum.width());
const int data_size = datum.channels() * datum.height() * datum.width();
int size_in_datum = std::max<int>(datum.data().size(),
datum.float_data_size());
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.add_data(0.);
}
// start collecting
LOG(INFO) << "Starting Iteration";
if (db_backend == "leveldb") { // leveldb
for (it->SeekToFirst(); it->Valid(); it->Next()) {
// just a dummy operation
datum.ParseFromString(it->value().ToString());
const string& data = datum.data();
size_in_datum = std::max<int>(datum.data().size(),
datum.float_data_size());
CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
size_in_datum;
if (data.size() != 0) {
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
}
} else {
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.set_data(i, sum_blob.data(i) +
static_cast<float>(datum.float_data(i)));
}
}
++count;
if (count % 10000 == 0) {
LOG(ERROR) << "Processed " << count << " files.";
}
}
} else if (db_backend == "lmdb") { // lmdb
CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
MDB_SUCCESS);
do {
// just a dummy operation
datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
const string& data = datum.data();
size_in_datum = std::max<int>(datum.data().size(),
datum.float_data_size());
CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
size_in_datum;
if (data.size() != 0) {
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
}
} else {
for (int i = 0; i < size_in_datum; ++i) {
sum_blob.set_data(i, sum_blob.data(i) +
static_cast<float>(datum.float_data(i)));
}
}
++count;
if (count % 10000 == 0) {
LOG(ERROR) << "Processed " << count << " files.";
}
} while (mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_NEXT)
== MDB_SUCCESS);
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
for (int i = 0; i < sum_blob.data_size(); ++i) {
sum_blob.set_data(i, sum_blob.data(i) / count);
}
caffe::Blob<float> vis;
vis.FromProto(sum_blob);
caffe::imshow(&vis, 1, "mean img");
cv::waitKey(0);
google::protobuf::RepeatedField<float>* tmp = sum_blob.mutable_data();
std::vector<float> mean_data(tmp->begin(), tmp->end());
double sum = std::accumulate(mean_data.begin(), mean_data.end(), 0.0);
double mean2 = sum / mean_data.size();
double sq_sum = std::inner_product(mean_data.begin(), mean_data.end(), mean_data.begin(), 0.0);
double stdev = std::sqrt(sq_sum / mean_data.size() - mean2 * mean2);
LOG(INFO) << "mean of mean image: " << mean2 << " std: " << stdev;
// Write to disk
LOG(INFO) << "Write to " << argv[2];
WriteProtoToBinaryFile(sum_blob, argv[2]);
// Clean up
if (db_backend == "leveldb") {
delete db;
} else if (db_backend == "lmdb") {
mdb_cursor_close(mdb_cursor);
mdb_close(mdb_env, mdb_dbi);
mdb_txn_abort(mdb_txn);
mdb_env_close(mdb_env);
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
return 0;
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
if (argc < 5) {
printf(
"Convert a set of images to the leveldb format used\n"
"as input for Caffe.\n"
"Usage:\n"
" convert_imageset ROOTFOLDER/ ANNOTATION DB_NAME"
" MODE[0-train, 1-val, 2-test] RANDOM_SHUFFLE_DATA[0 or 1, default 1] RESIZE_WIDTH[default 256] RESIZE_HEIGHT[default 256](0 indicates no resize)\n"
"The ImageNet dataset for the training demo is at\n"
" http://www.image-net.org/download-images\n");
return 0;
}
std::ifstream infile(argv[2]);
std::vector<Seg_Anno> annos;
std::set<string> fNames;
string filename;
int prop;
while (infile >> filename)
{
LOG(INFO)<<filename;
Seg_Anno seg_Anno;
seg_Anno.filename_ = filename;
for (int i = 0; i < LABEL_LEN; i++)
{
infile >> prop;
seg_Anno.pos_.push_back(prop);
}
if (fNames.find(filename)== fNames.end())
{
fNames.insert(filename);
annos.push_back(seg_Anno);
}
//debug
//if(annos.size() == 10)
// break;
}
if (argc < 6 || argv[5][0] != '0') {
// randomly shuffle data
LOG(INFO)<< "Shuffling data";
std::random_shuffle(annos.begin(), annos.end());
}
LOG(INFO)<< "A total of " << annos.size() << " images.";
leveldb::DB* db;
leveldb::Options options;
options.error_if_exists = true;
options.create_if_missing = true;
options.write_buffer_size = 268435456;
LOG(INFO)<< "Opening leveldb " << argv[3];
leveldb::Status status = leveldb::DB::Open(options, argv[3], &db);
CHECK(status.ok()) << "Failed to open leveldb " << argv[3];
string root_folder(argv[1]);
string fchannel_folder(argv[8]);
Datum datum;
int count = 0;
const int maxKeyLength = 256;
char key_cstr[maxKeyLength];
leveldb::WriteBatch* batch = new leveldb::WriteBatch();
int data_size;
bool data_size_initialized = false;
// resize to height * width
int width = RESIZE_LEN;
int height = RESIZE_LEN;
if (argc > 6) width = atoi(argv[6]);
if (argc > 7) height = atoi(argv[7]);
if (width == 0 || height == 0)
LOG(INFO) << "NO RESIZE SHOULD BE DONE";
else
LOG(INFO) << "RESIZE DIM: " << width << "*" << height;
for (int anno_id = 0; anno_id < annos.size(); ++anno_id)
{
string filename2 = parseString(annos[anno_id].filename_);
if (!MyReadImageToDatum(root_folder + "/" + annos[anno_id].filename_, fchannel_folder + "/" + filename2,
annos[anno_id].pos_, height, width, &datum))
{
continue;
}
if (!data_size_initialized)
{
data_size = datum.channels() * datum.height() * datum.width();
data_size_initialized = true;
}
else
{
const string& data = datum.data();
CHECK_EQ(data.size(), data_size)<< "Incorrect data field size " << data.size();
}
// sequential
snprintf(key_cstr, maxKeyLength, "%07d_%s", anno_id, annos[anno_id].filename_.c_str());
string value;
// get the value
datum.SerializeToString(&value);
batch->Put(string(key_cstr), value);
if (++count % 1000 == 0)
{
db->Write(leveldb::WriteOptions(), batch);
LOG(ERROR)<< "Processed " << count << " files.";
delete batch;
batch = new leveldb::WriteBatch();
}
}
// write the last batch
if (count % 1000 != 0) {
db->Write(leveldb::WriteOptions(), batch);
LOG(ERROR)<< "Processed " << count << " files.";
}
delete batch;
delete db;
return 0;
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
if (argc < 4) {
printf("Convert a set of images to the leveldb format used\n"
"as input for Caffe.\n"
"Usage:\n"
" convert_imageset ROOTFOLDER/ LISTFILE DB_NAME"
" RANDOM_SHUFFLE_DATA[0 or 1]\n"
"The ImageNet dataset for the training demo is at\n"
" http://www.image-net.org/download-images\n");
return 0;
}
std::ifstream infile(argv[2]);
std::vector<std::pair<string, int> > lines;
string filename;
int label;
while (infile >> filename >> label) {
lines.push_back(std::make_pair(filename, label));
}
if (argc == 5 && argv[4][0] == '1') {
// randomly shuffle data
LOG(INFO) << "Shuffling data";
std::random_shuffle(lines.begin(), lines.end());
}
LOG(INFO) << "A total of " << lines.size() << " images.";
leveldb::DB* db;
leveldb::Options options;
options.error_if_exists = true;
options.create_if_missing = true;
options.write_buffer_size = 268435456;
LOG(INFO) << "Opening leveldb " << argv[3];
leveldb::Status status = leveldb::DB::Open(
options, argv[3], &db);
CHECK(status.ok()) << "Failed to open leveldb " << argv[3];
string root_folder(argv[1]);
Datum datum;
int count = 0;
const int kMaxKeyLength = 256;
char key_cstr[kMaxKeyLength];
leveldb::WriteBatch* batch = new leveldb::WriteBatch();
int data_size;
bool data_size_initialized = false;
for (int line_id = 0; line_id < lines.size(); ++line_id) {
if (!ReadImageToDatum(root_folder + lines[line_id].first,
lines[line_id].second, &datum)) {
continue;
}
if (!data_size_initialized) {
data_size = datum.channels() * datum.height() * datum.width();
data_size_initialized = true;
} else {
const string& data = datum.data();
CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
<< data.size();
}
// sequential
snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
lines[line_id].first.c_str());
string value;
// get the value
datum.SerializeToString(&value);
batch->Put(string(key_cstr), value);
if (++count % 1000 == 0) {
db->Write(leveldb::WriteOptions(), batch);
LOG(ERROR) << "Processed " << count << " files.";
delete batch;
batch = new leveldb::WriteBatch();
}
}
// write the last batch
if (count % 1000 != 0) {
db->Write(leveldb::WriteOptions(), batch);
LOG(ERROR) << "Processed " << count << " files.";
}
delete batch;
delete db;
return 0;
}
void MyImageDataLayer<Dtype>::fetchData() {
Datum datum;
CHECK(prefetch_data_.count());
Dtype* top_data = prefetch_data_.mutable_cpu_data();
Dtype* top_label = prefetch_label_.mutable_cpu_data();
ImageDataParameter image_data_param = this->layer_param_.image_data_param();
const Dtype scale = image_data_param.scale();//image_data_layer相关参数
const int batch_size = 1;//image_data_param.batch_size(); 这里我们只需要一张图片
const int crop_size = image_data_param.crop_size();
const bool mirror = image_data_param.mirror();
const int new_height = image_data_param.new_height();
const int new_width = image_data_param.new_width();
if (mirror && crop_size == 0) {
LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
<< "set at the same time.";
}
// datum scales
const int channels = datum_channels_;
const int height = datum_height_;
const int width = datum_width_;
const int size = datum_size_;
const int lines_size = lines_.size();
const Dtype* mean = data_mean_.cpu_data();
for (int item_id = 0; item_id < batch_size; ++item_id) {//读取一图片
// get a blob
CHECK_GT(lines_size, lines_id_);
if (!ReadImageToDatum(lines_[lines_id_].first,
lines_[lines_id_].second,
new_height, new_width, &datum)) {
continue;
}
const string& data = datum.data();
if (crop_size) {
CHECK(data.size()) << "Image cropping only support uint8 data";
int h_off, w_off;
// We only do random crop when we do training.
h_off = (height - crop_size) / 2;
w_off = (width - crop_size) / 2;
// Normal copy 正常读取，把裁剪后的图片数据读给top_data
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < crop_size; ++h) {
for (int w = 0; w < crop_size; ++w) {
int top_index = ((item_id * channels + c) * crop_size + h)
* crop_size + w;
int data_index = (c * height + h + h_off) * width + w + w_off;
Dtype datum_element =
static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
top_data[top_index] = (datum_element - mean[data_index]) * scale;
}
}
}
} else {
// Just copy the whole data 正常读取，把图片数据读给top_data
if (data.size()) {
for (int j = 0; j < size; ++j) {
Dtype datum_element =
static_cast<Dtype>(static_cast<uint8_t>(data[j]));
top_data[item_id * size + j] = (datum_element - mean[j]) * scale;
}
} else {
for (int j = 0; j < size; ++j) {
top_data[item_id * size + j] =
(datum.float_data(j) - mean[j]) * scale;
}
}
}
top_label[item_id] = datum.label();//读取该图片的标签
}
}
void DataTransformer<Dtype>::Transform(const int batch_item_id,
const Datum& datum,
const Dtype* mean,
Dtype* transformed_data) {
const string& data = datum.data();
const int channels = datum.channels();
const int height = datum.height();
const int width = datum.width();
const int size = datum.channels() * datum.height() * datum.width();
const int crop_size = param_.crop_size();
const bool mirror = param_.mirror();
const Dtype scale = param_.scale();
if (mirror && crop_size == 0) {
LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
<< "set at the same time.";
}
if (crop_size) {
CHECK(data.size()) << "Image cropping only support uint8 data";
int h_off, w_off;
// We only do random crop when we do training.
if (phase_ == Caffe::TRAIN) {
h_off = Rand() % (height - crop_size);
w_off = Rand() % (width - crop_size);
} else {
h_off = (height - crop_size) / 2;
w_off = (width - crop_size) / 2;
}
if (mirror && Rand() % 2) {
// Copy mirrored version
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < crop_size; ++h) {
for (int w = 0; w < crop_size; ++w) {
int data_index = (c * height + h + h_off) * width + w + w_off;
int top_index = ((batch_item_id * channels + c) * crop_size + h)
* crop_size + (crop_size - 1 - w);
Dtype datum_element =
static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
transformed_data[top_index] =
(datum_element - mean[data_index]) * scale;
}
}
}
} else {
// Normal copy
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < crop_size; ++h) {
for (int w = 0; w < crop_size; ++w) {
int top_index = ((batch_item_id * channels + c) * crop_size + h)
* crop_size + w;
int data_index = (c * height + h + h_off) * width + w + w_off;
Dtype datum_element =
static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
transformed_data[top_index] =
(datum_element - mean[data_index]) * scale;
}
}
}
}
} else {
// we will prefer to use data() first, and then try float_data()
if (data.size()) {
for (int j = 0; j < size; ++j) {
Dtype datum_element =
static_cast<Dtype>(static_cast<uint8_t>(data[j]));
transformed_data[j + batch_item_id * size] =
(datum_element - mean[j]) * scale;
}
} else {
for (int j = 0; j < size; ++j) {
transformed_data[j + batch_item_id * size] =
(datum.float_data(j) - mean[j]) * scale;
}
}
}
}
void DataTransformer<Dtype>::Transform(const Datum& datum,
Dtype* transformed_data) {
const string& data = datum.data();
const int datum_channels = datum.channels();
const int datum_height = datum.height();
const int datum_width = datum.width();
const int crop_size = param_.crop_size();
const Dtype scale = param_.scale();
const bool do_mirror = param_.mirror() && Rand(2);
const bool has_mean_file = param_.has_mean_file();
const bool has_uint8 = data.size() > 0;
const bool has_mean_values = mean_values_.size() > 0;
CHECK_GT(datum_channels, 0);
CHECK_GE(datum_height, crop_size);
CHECK_GE(datum_width, crop_size);
Dtype* mean = NULL;
if (has_mean_file) {
CHECK_EQ(datum_channels, data_mean_.channels());
CHECK_EQ(datum_height, data_mean_.height());
CHECK_EQ(datum_width, data_mean_.width());
mean = data_mean_.mutable_cpu_data();
}
if (has_mean_values) {
CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) <<
"Specify either 1 mean_value or as many as channels: " << datum_channels;
if (datum_channels > 1 && mean_values_.size() == 1) {
// Replicate the mean_value for simplicity
for (int c = 1; c < datum_channels; ++c) {
mean_values_.push_back(mean_values_[0]);
}
}
}
int height = datum_height;
int width = datum_width;
int h_off = 0;
int w_off = 0;
if (crop_size) {
height = crop_size;
width = crop_size;
// We only do random crop when we do training.
if (phase_ == TRAIN) {
h_off = Rand(datum_height - crop_size + 1);
w_off = Rand(datum_width - crop_size + 1);
} else {
h_off = (datum_height - crop_size) / 2;
w_off = (datum_width - crop_size) / 2;
}
}
Dtype datum_element;
int top_index, data_index;
for (int c = 0; c < datum_channels; ++c) {
for (int h = 0; h < height; ++h) {
for (int w = 0; w < width; ++w) {
data_index = (c * datum_height + h_off + h) * datum_width + w_off + w;
if (do_mirror) {
top_index = (c * height + h) * width + (width - 1 - w);
} else {
top_index = (c * height + h) * width + w;
}
if (has_uint8) {
datum_element =
static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
} else {
datum_element = datum.float_data(data_index);
}
if (has_mean_file) {
transformed_data[top_index] =
(datum_element - mean[data_index]) * scale;
} else {
if (has_mean_values) {
transformed_data[top_index] =
(datum_element - mean_values_[c]) * scale;
} else {
transformed_data[top_index] = datum_element * scale;
}
}
}
}
}
}
int main(int argc, char** argv) {
::google::InitGoogleLogging(argv[0]);
#ifndef GFLAGS_GFLAGS_H_
namespace gflags = google;
#endif
gflags::SetUsageMessage("Convert a set of images to the leveldb/lmdb\n"
"format used as input for Caffe.\n"
"Usage:\n"
" convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAME\n"
"The ImageNet dataset for the training demo is at\n"
" http://www.image-net.org/download-images\n");
gflags::ParseCommandLineFlags(&argc, &argv, true);
if (argc != 4) {
gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/convert_imageset");
return 1;
}
bool is_color = !FLAGS_gray;
std::ifstream infile(argv[2]);
std::vector<std::pair<string, int> > lines;
string filename;
int label;
while (infile >> filename >> label) {
lines.push_back(std::make_pair(filename, label));
}
if (FLAGS_shuffle) {
// randomly shuffle data
LOG(INFO) << "Shuffling data";
shuffle(lines.begin(), lines.end());
}
LOG(INFO) << "A total of " << lines.size() << " images.";
const string& db_backend = FLAGS_backend;
const char* db_path = argv[3];
int resize_height = std::max<int>(0, FLAGS_resize_height);
int resize_width = std::max<int>(0, FLAGS_resize_width);
// Open new db
// lmdb
MDB_env *mdb_env;
MDB_dbi mdb_dbi;
MDB_val mdb_key, mdb_data;
MDB_txn *mdb_txn;
// leveldb
leveldb::DB* db;
leveldb::Options options;
options.error_if_exists = true;
options.create_if_missing = true;
options.write_buffer_size = 268435456;
leveldb::WriteBatch* batch = NULL;
// Open db
if (db_backend == "leveldb") { // leveldb
LOG(INFO) << "Opening leveldb " << db_path;
leveldb::Status status = leveldb::DB::Open(
options, db_path, &db);
CHECK(status.ok()) << "Failed to open leveldb " << db_path
<< ". Is it already existing?";
batch = new leveldb::WriteBatch();
} else if (db_backend == "lmdb") { // lmdb
LOG(INFO) << "Opening lmdb " << db_path;
CHECK_EQ(mkdir(db_path, 0744), 0)
<< "mkdir " << db_path << "failed";
CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS) // 1TB
<< "mdb_env_set_mapsize failed";
CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)
<< "mdb_env_open failed";
CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
<< "mdb_txn_begin failed";
CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
<< "mdb_open failed. Does the lmdb already exist? ";
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
// Storing to db
string root_folder(argv[1]);
Datum datum;
int count = 0;
const int kMaxKeyLength = 256;
char key_cstr[kMaxKeyLength];
int data_size;
bool data_size_initialized = false;
for (int line_id = 0; line_id < lines.size(); ++line_id) {
if (!ReadImageToDatum(root_folder + lines[line_id].first,
lines[line_id].second, resize_height, resize_width, is_color, &datum)) {
continue;
}
if (!data_size_initialized) {
data_size = datum.channels() * datum.height() * datum.width();
data_size_initialized = true;
} else {
const string& data = datum.data();
CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
<< data.size();
}
// sequential
snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
lines[line_id].first.c_str());
string value;
datum.SerializeToString(&value);
string keystr(key_cstr);
// Put in db
if (db_backend == "leveldb") { // leveldb
batch->Put(keystr, value);
} else if (db_backend == "lmdb") { // lmdb
mdb_data.mv_size = value.size();
mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
mdb_key.mv_size = keystr.size();
mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
<< "mdb_put failed";
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
if (++count % 1000 == 0) {
// Commit txn
if (db_backend == "leveldb") { // leveldb
db->Write(leveldb::WriteOptions(), batch);
delete batch;
batch = new leveldb::WriteBatch();
} else if (db_backend == "lmdb") { // lmdb
CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
<< "mdb_txn_commit failed";
CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
<< "mdb_txn_begin failed";
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
LOG(ERROR) << "Processed " << count << " files.";
}
}
// write the last batch
if (count % 1000 != 0) {
if (db_backend == "leveldb") { // leveldb
db->Write(leveldb::WriteOptions(), batch);
delete batch;
delete db;
} else if (db_backend == "lmdb") { // lmdb
CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
mdb_close(mdb_env, mdb_dbi);
mdb_env_close(mdb_env);
} else {
LOG(FATAL) << "Unknown db backend " << db_backend;
}
LOG(ERROR) << "Processed " << count << " files.";
}
return 0;
}
