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Multi-tags using two different LMDBs

I am new to the caffe framework and I would like to use caffe to implement multi-tag training. I use two LMDBs to store data and tags, respectively. LMDB data has a dimension of Nx1xHxW, and the LMDB label has a dimension of Nx1x1x3. Labels are floating point data.

The text file is as follows:

5911 3
train/train_data/4224.bmp        13         0        12
train/train_data/3625.bmp        11         3         7
...                              ...

I use C ++ to create LMDB. My main.cpp:

#include <algorithm>
#include <fstream>  // NOLINT(readability/streams)
#include <string>
#include <utility>
#include <vector>
#include <QImage>

#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/rng.hpp"

#include <boost/filesystem.hpp>
#include <iomanip>
#include <iostream>  // NOLINT(readability/streams)
#include <string>

#include "google/protobuf/message.h"

#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/format.hpp"

#ifndef CAFFE_TMP_DIR_RETRIES
#define CAFFE_TMP_DIR_RETRIES 100
#endif

using namespace caffe;  // NOLINT(build/namespaces)
using std::pair;
using boost::scoped_ptr;

const char *dat_lab="/home/mul/caffe-master/examples/2D_3D/new/info/train.data";
string data_db="/home/mul/caffe-master/examples/2D_3D/new/2D_3D_data_leveldb";
string label_db="/home/mul/caffe-master/examples/2D_3D/new/2D_3D_label_leveldb";
string root="/home/mul/caffe-master/examples/2D_3D/new/";
string path;

int main()
{

    //Create data DB
    scoped_ptr<db::DB> dat_db(db::GetDB("leveldb"));
    dat_db->Open(data_db, db::NEW);
    scoped_ptr<db::Transaction> dat_txn(dat_db->NewTransaction());

    //Create label DB
    scoped_ptr<db::DB> lab_db(db::GetDB("leveldb"));
    lab_db->Open(label_db, db::NEW);
    scoped_ptr<db::Transaction> lab_txn(lab_db->NewTransaction());

    //Storing to db
    Datum dat_datum,lab_datum;
    int count=0;

    std::ifstream infile(dat_lab);
    std::string filename;
    const char *dataname;
    int dataNum;
    int labelcount;
    QImage img;
    infile>>dataNum>>labelcount;
    LOG(INFO) << "A total of " << dataNum<< " images.";

    for (int line_id = 0; line_id < dataNum; ++line_id)
    {
        infile>>filename;
        path=root+filename;
        dataname=path.c_str();
        img.load(dataname);

        dat_datum.set_channels(1);
        dat_datum.set_height(img.height());
        dat_datum.set_width(img.width());
        dat_datum.clear_data();
        dat_datum.clear_float_data();

        int datum_channels = dat_datum.channels();
        int datum_height = dat_datum.height();
        int datum_width = dat_datum.width();
        int datum_size = datum_channels * datum_height * datum_width;
        std::string buffer(datum_size, ' ');
        const uchar* ptr = img.bits();
        int img_index = 0;
        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;
                    buffer[datum_index] = static_cast<char>(ptr[img_index++]);
                }
            }
        }
        dat_datum.set_data(buffer);


        lab_datum.set_channels(labelcount);
        lab_datum.set_height(1);
        lab_datum.set_width(1);
        lab_datum.clear_data();
        lab_datum.clear_float_data();
        for(int i=0;i<labelcount;++i)
        {
            float mid;
            infile>>mid;
            lab_datum.add_float_data(mid);
        }

        // sequential
        string key_str = caffe::format_int(line_id, 8);

        // Put in db
        string out;
        CHECK(dat_datum.SerializeToString(&out));
        dat_txn->Put(key_str, out);
        CHECK(lab_datum.SerializeToString(&out));
        lab_txn->Put(key_str, out);

        if (++count % 1000 == 0)
        {
            // Commit db
            dat_txn->Commit();
            dat_txn.reset(dat_db->NewTransaction());
            lab_txn->Commit();
            lab_txn.reset(lab_db->NewTransaction());
            LOG(INFO) << "Processed " << count << " files.";
        }
    }
    // write the last batch
    if (count % 1000 != 0)
    {
        dat_txn->Commit();
        lab_txn->Commit();
        LOG(INFO) << "Processed " << count << " files.";
    }
    return 0;
}

Two LMDBs can be created successfully. But when I use caffe to implement training using two LMDBs, the result is always incorrect. The loss level is EUCLIDEAN_LOSS and the loss cannot go down. I do not know if the code that can create two LMDBs is wrong. Who can help me? Thanks, whatever.

+4
1

, , , :

  • .cpp LEVELDB LMDB, , , , .
  • " LMDB" , Nx3x1x1 Nx1x1x3 (NumberxChannelxWidHeight).
  • , - SGD, , , . , . cpp "train.data".
  • , , , , lmdb/leveldb, DataLayer caffe ( ). , , prototxt, . , , .

, MultiTaskData MultiTaskDataLayer, , :

    name: "AgeNet"
    layer {
        name: "Age"
        type: "MultiTaskData"
        top: "data"
        top: "age_label"
        top: "gender_label"
        data_param { 
            source: "age_gender_classification_0_60p_train_leveldb"   
            batch_size: 60 
            task_num: 2
            label_dimension: 1
            label_dimension: 1
        }
        transform_param {
            scale: 0.00390625
            crop_size: 60
            mirror: true
        }
        include:{ phase: TRAIN }
    }
    layer { 
        name: "cls_age" 
        type: "InnerProduct"
        bottom: "data"  
        top: "cls_age" 
        param {
            lr_mult: 1
            decay_mult: 1
        }
        param {
            lr_mult: 2
            decay_mult: 0
        }
        inner_product_param {
            num_output: 7
            weight_filler {
            type: "xavier"
            }    
        bias_filler {      
            type: "constant"
            }  
        }
    }
    layer {  
        name: "age_loss"  
        type: "SoftmaxWithLoss"  
        bottom: "cls_age" 
        bottom: "age_label"
        top: "age_loss"
        include:{ phase: TRAIN }
    }
    layer { 
        name: "cls_gender" 
        type: "InnerProduct"
        bottom: "data"  
        top: "cls_gender" 
        param {
            lr_mult: 1
            decay_mult: 1
        }
        param {
            lr_mult: 2
            decay_mult: 0
        }
        inner_product_param {
            num_output: 2
            weight_filler {
                type: "xavier"
            }    
            bias_filler {      
                type: "constant"
            }  
        }
    }
    layer {  
        name: "gender_loss"  
        type: "SoftmaxWithLoss"  
        bottom: "cls_gender" 
        bottom: "gender_label"
        top: "gender_loss"
        include:{ phase: TRAIN }
    }
+2

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