转载自：多任务深度学习
基于Caffe实现多任务学习的小样例
作者：程程
链接：https://zhuanlan.zhihu.com/p/22190532
来源：知乎
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。
基于Caffe实现多任务学习的小样例
本节在目前广泛使用的深度学习开源框架Caffe的基础上实现多任务深度学习算法所需的多维标签输入。默认的，Caffe中的Data层只支持单维标签，为了支持多维标签，首先修改Caffe中的convert_imageset.cpp以支持多标签：
这样我们就有了多任务的深度学习的基础部分数据输入。为了向上兼容Caffe框架，本文摒弃了部分开源实现增加Data层标签维度选项并修改Data层代码的做法，直接使用两个Data层将数据读入，即分别读入数据和多维标签，接下来介绍对应的网络结构文件prototxt的修改，注意红色的注释部分。
特别的，slice层对多维的标签进行了切分，为每个任务输出了单独的标签。
另外一个值得讨论的是每个任务的权重设置，在本文实践中五个任务设置为等权重loss_weight:0.2。一般的，建议所有任务的权重值相加为1，如果这个数值不设置，可能会导致网络收敛不稳定，这是因为多任务学习中对不同任务的梯度进行累加，导致梯度过大，甚至可能引发参数溢出错误导致网络训练失败。
作者：程程
链接：https://zhuanlan.zhihu.com/p/22190532
来源：知乎
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。
多任务损失函数层的网络结构示意图如下图所示：
| // This program converts a set of images to a lmdb/leveldb by storing them |
| // as Datum proto buffers. |
| // Usage: |
| // convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAME |
| // |
| // where ROOTFOLDER is the root folder that holds all the images, and LISTFILE |
| // should be a list of files as well as their labels, in the format as |
| // subfolder1/file1.JPEG 7 |
| // .... |
| //#ifdef MULTILABEL |
| #include <algorithm> |
| #include <fstream> // NOLINT(readability/streams) |
| #include <string> |
| #include <utility> |
| #include <vector> |
| #include "boost/scoped_ptr.hpp" |
| #include "gflags/gflags.h" |
| #include "glog/logging.h" |
| #include "caffe/proto/caffe.pb.h" |
| #include "caffe/util/db.hpp" |
| #include "caffe/util/format.hpp" |
| #include "caffe/util/io.hpp" |
| #include "caffe/util/rng.hpp" |
| using namespace caffe; // NOLINT(build/namespaces) |
| using std::pair; |
| using boost::scoped_ptr; |
| DEFINE_bool(gray, false, |
| "When this option is on, treat images as grayscale ones"); |
| DEFINE_bool(shuffle, false, |
| "Randomly shuffle the order of images and their labels"); |
| DEFINE_string(backend, "lmdb", |
| "The backend {lmdb, leveldb} for storing the result"); |
| DEFINE_int32(resize_width, 0, "Width images are resized to"); |
| DEFINE_int32(resize_height, 0, "Height images are resized to"); |
| DEFINE_bool(check_size, false, |
| "When this option is on, check that all the datum have the same size"); |
| DEFINE_bool(encoded, false, |
| "When this option is on, the encoded image will be save in datum"); |
| DEFINE_string(encode_type, "", |
| "Optional: What type should we encode the image as ('png','jpg',...)."); |
| 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/lmdbn" |
| "format used as input for Caffe.n" |
| "Usage:n" |
| " convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAMEn" |
| "The ImageNet dataset for the training demo is atn" |
| " http://www.image-net.org/download-imagesn"); |
| gflags::ParseCommandLineFlags(&argc, &argv, true); |
| if (argc < 6) { |
| 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; |
| const string encode_type = FLAGS_encode_type; |
| std::ifstream infile(argv[2]); |
| std::vector<std::pair<std::string, std::vector<float>> > lines; |
| std::string filename; |
| std::string label_count_string = argv[5]; |
| int label_count = std::atoi(label_count_string.c_str()); |
| std::vector<float> label(label_count); |
| while (infile >> filename) |
| { |
| for (int i = 0; i < label_count;i++) |
| { |
| infile >> label[i]; |
| } |
| 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_image(db::GetDB(FLAGS_backend)); |
| scoped_ptr<db::DB> db_label(db::GetDB(FLAGS_backend)); |
| db_image->Open(argv[3], db::NEW); |
| db_label->Open(argv[4], db::NEW); |
| scoped_ptr<db::Transaction> txn_image(db_image->NewTransaction()); |
| scoped_ptr<db::Transaction> txn_label(db_label->NewTransaction()); |
| // Storing to db |
| std::string root_folder(argv[1]); |
| Datum datum_label; |
| Datum datum_image; |
| int count = 0; |
| int data_size_label = 0; |
| int data_size_image = 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[0], resize_height, resize_width, is_color, |
| enc, &datum_image); |
| if (status == false) continue; |
| datum_label.set_height(1); |
| datum_label.set_width(1); |
| datum_label.set_channels(label_count); |
| int count_tmp = datum_label.float_data_size(); |
| for (int index_label = 0; index_label < lines[line_id].second.size(); index_label++) |
| { |
| float tmp_float_value = lines[line_id].second[index_label]; |
| datum_label.add_float_data(tmp_float_value); |
| } |
| if (check_size) { |
| if (!data_size_initialized) { |
| data_size_label = datum_label.channels() * datum_label.height() * datum_label.width(); |
| data_size_image = datum_image.channels() * datum_image.height() * datum_image.width(); |
| data_size_initialized = true; |
| } |
| else { |
| const std::string& data_label = datum_label.data(); |
| CHECK_EQ(data_label.size(), data_size_label) << "Incorrect data field size " |
| << data_label.size(); |
| const std::string& data_image = data_image.data(); |
| CHECK_EQ(data_image.size(), data_size_image) << "Incorrect data field size " |
| << data_image.size(); |
| } |
| } |
| // sequential |
| string key_str_image = caffe::format_int(line_id, 8) + "_" + lines[line_id].first; |
| string key_str_label = caffe::format_int(line_id, 8) + "label_" + lines[line_id].first; |
| // Put in db |
| string out_label; |
| string out_image; |
| CHECK(datum_label.SerializeToString(&out_label)); |
| CHECK(datum_image.SerializeToString(&out_image)); |
| datum_label.clear_float_data(); |
| txn_label->Put(key_str_label, out_label); |
| txn_image->Put(key_str_image, out_image); |
| if (++count % 1000 == 0) { |
| // Commit db |
| txn_image->Commit(); |
| txn_image.reset(db_image->NewTransaction()); |
| txn_label->Commit(); |
| txn_label.reset(db_label->NewTransaction()); |
| LOG(INFO) << "Processed " << count << " files."; |
| } |
| } |
| // write the last batch |
| if (count % 1000 != 0) { |
| txn_label->Commit(); |
| txn_image->Commit(); |
| LOG(INFO) << "Processed " << count << " files."; |
| } |
| #else |
| LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV."; |
| #endif // USE_OPENCV |
| return 0; |
| } |
| //#endif |
最后
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