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Diffstat (limited to 'src/TRecurrentCu.cc')
| -rw-r--r-- | src/TRecurrentCu.cc | 420 |
1 files changed, 420 insertions, 0 deletions
diff --git a/src/TRecurrentCu.cc b/src/TRecurrentCu.cc new file mode 100644 index 0000000..f05008d --- /dev/null +++ b/src/TRecurrentCu.cc @@ -0,0 +1,420 @@ + +/*************************************************************************** + * copyright : (C) 2011 by Karel Vesely,UPGM,FIT,VUT,Brno * + * email : iveselyk@fit.vutbr.cz * + *************************************************************************** + * * + * This program is free software; you can redistribute it and/or modify * + * it under the terms of the APACHE License as published by the * + * Apache Software Foundation; either version 2.0 of the License, * + * or (at your option) any later version. * + * * + ***************************************************************************/ + +#define SVN_DATE "$Date: 2011-10-18 12:42:04 +0200 (Tue, 18 Oct 2011) $" +#define SVN_AUTHOR "$Author: iveselyk $" +#define SVN_REVISION "$Revision: 86 $" +#define SVN_ID "$Id: TRecurrentCu.cc 86 2011-10-18 10:42:04Z iveselyk $" + +#define MODULE_VERSION "1.0.0 "__TIME__" "__DATE__" "SVN_ID + + + + +/*** TNetLib includes */ +#include "Error.h" +#include "Timer.h" +#include "Features.h" +#include "Labels.h" +#include "Common.h" +#include "MlfStream.h" +#include "UserInterface.h" +#include "Timer.h" + +/*** TNet includes */ +#include "cuObjectiveFunction.h" +#include "cuNetwork.h" +#include "cuRecurrent.h" + +/*** STL includes */ +#include <iostream> +#include <sstream> +#include <numeric> + + + + +////////////////////////////////////////////////////////////////////// +// DEFINES +// + +#define SNAME "TNET" + +using namespace TNet; + +void usage(const char* progname) +{ + const char *tchrptr; + if ((tchrptr = strrchr(progname, '\\')) != NULL) progname = tchrptr+1; + if ((tchrptr = strrchr(progname, '/')) != NULL) progname = tchrptr+1; + fprintf(stderr, +"\n%s version " MODULE_VERSION "\n" +"\nUSAGE: %s [options] DataFiles...\n\n" +"\n:TODO:\n\n" +" Option Default\n\n" +" -c Enable crossvalidation off\n" +" -m file Set label map of NN outputs \n" +" -n f Set learning rate to f 0.06\n" +" -o ext Set target model ext None\n" +" -A Print command line arguments Off\n" +" -C cf Set config file to cf Default\n" +" -D Display configuration variables Off\n" +" -H mmf Load NN macro file \n" +" -I mlf Load master label file mlf \n" +" -L dir Set input label (or net) dir Current\n" +" -M dir Dir to write NN macro files Current\n" +" -O fn Objective function [mse,xent] xent\n" +" -S file Set script file None\n" +" -T N Set trace flags to N 0\n" +" -V Print version information Off\n" +" -X ext Set input label file ext lab\n" +"\n" +"BUNCHSIZE CACHESIZE CROSSVALIDATE FEATURETRANSFORM LEARNINGRATE LEARNRATEFACTORS MLFTRANSC MOMENTUM NATURALREADORDER OBJECTIVEFUNCTION OUTPUTLABELMAP PRINTCONFIG PRINTVERSION RANDOMIZE SCRIPT SEED SOURCEMLF SOURCEMMF SOURCETRANSCDIR SOURCETRANSCEXT TARGETMMF TARGETMODELDIR TARGETMODELEXT TRACE WEIGHTCOST\n" +"\n" +"STARTFRMEXT ENDFRMEXT CMEANDIR CMEANMASK VARSCALEDIR VARSCALEMASK VARSCALEFN TARGETKIND DERIVWINDOWS DELTAWINDOW ACCWINDOW THIRDWINDOW\n" +"\n" +" %s is Copyright (C) 2010-2011 Karel Vesely\n" +" licensed under the APACHE License, version 2.0\n" +" Bug reports, feedback, etc, to: iveselyk@fit.vutbr.cz\n" +"\n", progname, progname, progname); + exit(-1); +} + + + + + + +/////////////////////////////////////////////////////////////////////// +// MAIN FUNCTION +// + + +int main(int argc, char *argv[]) try +{ + const char* p_option_string = + " -m r OUTPUTLABELMAP" + " -n r LEARNINGRATE" + " -D n PRINTCONFIG=TRUE" + " -H l SOURCEMMF" + " -I r SOURCEMLF" + " -L r SOURCETRANSCDIR" + " -S l SCRIPT" + " -T r TRACE" + " -V n PRINTVERSION=TRUE" + " -X r SOURCETRANSCEXT"; + + + UserInterface ui; + FeatureRepository feature_repo; + LabelRepository label_repo; + CuNetwork network; + CuNetwork transform_network; + CuObjectiveFunction* p_obj_function = NULL; + Timer timer; + Timer timer_frontend; + double time_frontend = 0.0; + + const char* p_source_mmf_file; + const char* p_input_transform; + const char* p_targetmmf; + + const char* p_script; + const char* p_output_label_map; + + BaseFloat learning_rate; + const char* learning_rate_factors; + BaseFloat momentum; + BaseFloat weightcost; + int bptt; + CuObjectiveFunction::ObjFunType obj_fun_id; + + const char* p_source_mlf_file; + const char* p_src_lbl_dir; + const char* p_src_lbl_ext; + + bool cross_validate; + + int trace; + + // variables for feature repository + bool swap_features; + int target_kind; + int deriv_order; + int* p_deriv_win_lenghts; + int start_frm_ext; + int end_frm_ext; + char* cmn_path; + char* cmn_file; + const char* cmn_mask; + char* cvn_path; + char* cvn_file; + const char* cvn_mask; + const char* cvg_file; + + + // OPTION PARSING .......................................................... + // use the STK option parsing + if (argc == 1) { usage(argv[0]); return 1; } + int args_parsed = ui.ParseOptions(argc, argv, p_option_string, SNAME); + + + // OPTION RETRIEVAL ........................................................ + // extract the feature parameters + swap_features = !ui.GetBool(SNAME":NATURALREADORDER", TNet::IsBigEndian()); + + target_kind = ui.GetFeatureParams(&deriv_order, &p_deriv_win_lenghts, + &start_frm_ext, &end_frm_ext, &cmn_path, &cmn_file, &cmn_mask, + &cvn_path, &cvn_file, &cvn_mask, &cvg_file, SNAME":", 0); + + + // extract other parameters + p_source_mmf_file = ui.GetStr(SNAME":SOURCEMMF", NULL); + p_input_transform = ui.GetStr(SNAME":FEATURETRANSFORM", NULL); + + p_targetmmf = ui.GetStr(SNAME":TARGETMMF", NULL); + + p_script = ui.GetStr(SNAME":SCRIPT", NULL); + p_output_label_map = ui.GetStr(SNAME":OUTPUTLABELMAP", NULL); + + learning_rate = ui.GetFlt(SNAME":LEARNINGRATE" , 0.06f); + learning_rate_factors = ui.GetStr(SNAME":LEARNRATEFACTORS", NULL); + momentum = ui.GetFlt(SNAME":MOMENTUM" , 0.0); + weightcost = ui.GetFlt(SNAME":WEIGHTCOST" , 0.0); + bptt = ui.GetInt(SNAME":BPTT" , 4); + + obj_fun_id = static_cast<CuObjectiveFunction::ObjFunType>( + ui.GetEnum(SNAME":OBJECTIVEFUNCTION", + CuObjectiveFunction::CROSS_ENTROPY, //< default + "xent", CuObjectiveFunction::CROSS_ENTROPY, + "mse", CuObjectiveFunction::MEAN_SQUARE_ERROR + )); + + + + p_source_mlf_file = ui.GetStr(SNAME":SOURCEMLF", NULL); + p_src_lbl_dir = ui.GetStr(SNAME":SOURCETRANSCDIR", NULL); + p_src_lbl_ext = ui.GetStr(SNAME":SOURCETRANSCEXT", "lab"); + + cross_validate = ui.GetBool(SNAME":CROSSVALIDATE", false); + + trace = ui.GetInt(SNAME":TRACE", 0); + //if(trace&1) { + CuDevice::Instantiate().Verbose(true); + //} + + + //throw away... + ui.GetInt(SNAME":BUNCHSIZE", 256); + ui.GetInt(SNAME":CACHESIZE", 12800); + ui.GetBool(SNAME":RANDOMIZE", true); + + + // process the parameters + if(ui.GetBool(SNAME":PRINTCONFIG", false)) { + std::cout << std::endl; + ui.PrintConfig(std::cout); + std::cout << std::endl; + } + if(ui.GetBool(SNAME":PRINTVERSION", false)) { + std::cout << std::endl; + std::cout << "======= "MODULE_VERSION" =======" << std::endl; + std::cout << std::endl; + } + ui.CheckCommandLineParamUse(); + + + // the rest of the parameters are the feature files + for (; args_parsed < argc; args_parsed++) { + feature_repo.AddFile(argv[args_parsed]); + } + + //************************************************************************** + //************************************************************************** + // OPTION PARSING DONE ..................................................... + + + //read the input transform network + if(NULL != p_input_transform) { + if(trace&1) TraceLog(std::string("Reading input transform network: ")+p_input_transform); + transform_network.ReadNetwork(p_input_transform); + } + + + //read the neural network + if(NULL != p_source_mmf_file) { + if(trace&1) TraceLog(std::string("Reading network: ")+p_source_mmf_file); + network.ReadNetwork(p_source_mmf_file); + } else { + Error("Source MMF must be specified [-H]"); + } + + + // initialize the feature repository + feature_repo.Init( + swap_features, start_frm_ext, end_frm_ext, target_kind, + deriv_order, p_deriv_win_lenghts, + cmn_path, cmn_mask, cvn_path, cvn_mask, cvg_file + ); + if(NULL != p_script) { + feature_repo.AddFileList(p_script); + } else { + Warning("WARNING: The script file is missing [-S]"); + } + + // initialize the label repository + if(NULL == p_source_mlf_file) + Error("Source mlf file file is missing [-I]"); + if(NULL == p_output_label_map) + Error("Output label map is missing [-m]"); + label_repo.Init(p_source_mlf_file, p_output_label_map, p_src_lbl_dir, p_src_lbl_ext); + + //get objective function instance + p_obj_function = CuObjectiveFunction::Factory(obj_fun_id); + + //set the learnrate, etc + network.SetLearnRate(learning_rate, learning_rate_factors); + network.SetMomentum(momentum); + network.SetWeightcost(weightcost); + + //set the BPTT order + for(int i=0; i<network.Layers(); i++) { + if(network.Layer(i).GetType() == CuComponent::RECURRENT) { + dynamic_cast<CuRecurrent&>(network.Layer(i)).BpttOrder(bptt); + } + } + + + //********************************************************************** + //********************************************************************** + // INITIALIZATION DONE ................................................. + // + // Start training + timer.Start(); + if(cross_validate) { + std::cout << "===== TRecurrentCu CROSSVAL STARTED =====" << std::endl; + } else { + std::cout << "===== TRecurrentCu TRAINING STARTED =====" << std::endl; + } + + feature_repo.Rewind(); + + //********************************************************************** + //********************************************************************** + // MAIN LOOP + // + int frames = 0; + Matrix<BaseFloat> targets_host; + CuMatrix<BaseFloat> feats, output, targets, globerr; + for(feature_repo.Rewind(); !feature_repo.EndOfList(); feature_repo.MoveNext()) { + + timer_frontend.Start(); + + Matrix<BaseFloat> feats_host, globerr_host; + CuMatrix<BaseFloat> feats_original; + CuMatrix<BaseFloat> feats_expanded; + + //read feats, perfrom feature transform + feature_repo.ReadFullMatrix(feats_host); + feats_original.CopyFrom(feats_host); + transform_network.Propagate(feats_original,feats_expanded); + + //trim the start/end context + int rows = feats_expanded.Rows()-start_frm_ext-end_frm_ext; + feats.Init(rows,feats_expanded.Cols()); + feats.CopyRows(rows,start_frm_ext,feats_expanded,0); + + timer_frontend.End(); time_frontend += timer_frontend.Val(); + + //read the targets + label_repo.GenDesiredMatrix(targets_host,feats.Rows(), + feature_repo.CurrentHeader().mSamplePeriod, + feature_repo.Current().Logical().c_str()); + targets.CopyFrom(targets_host); + + //reset the history context + for(int i=0; i<network.Layers(); i++) { + if(network.Layer(i).GetType() == CuComponent::RECURRENT) { + dynamic_cast<CuRecurrent&>(network.Layer(i)).ClearHistory(); + } + } + + CuMatrix<BaseFloat> input_row(1,feats.Cols()); + CuMatrix<BaseFloat> output_row(1,network.GetNOutputs()); + CuMatrix<BaseFloat> target_row(1,network.GetNOutputs()); + CuMatrix<BaseFloat> error_row(1,network.GetNOutputs()); + for(size_t frm=0; frm<feats.Rows(); frm++) { + //select data rows + input_row.CopyRows(1,frm,feats,0); + target_row.CopyRows(1,frm,targets,0); + + //forward + network.Propagate(input_row,output_row); + + //xetropy + p_obj_function->Evaluate(output_row,target_row,error_row); + + if(!cross_validate) { + //backward + network.Backpropagate(error_row); + } + } + + frames += feats.Rows(); + std::cout << "." << std::flush; + } + + + + //********************************************************************** + //********************************************************************** + // TRAINING FINISHED ................................................. + // + // Let's store the network, report the log + + + if(cross_validate) { + if(trace&1) TraceLog("Crossval finished"); + } else { + if(trace&1) TraceLog("Training finished"); + } + + //write the network + if(!cross_validate) { + if (NULL != p_targetmmf) { + if(trace&1) TraceLog(std::string("Writing network: ")+p_targetmmf); + network.WriteNetwork(p_targetmmf); + } else { + Error("forgot to specify --TARGETMMF argument"); + } + } + + timer.End(); + std::cout << std::endl; + std::cout << "===== TRecurrentCu FINISHED ( " << timer.Val() << "s ) " + << "[FPS:" << float(frames) / timer.Val() + << ",RT:" << 1.0f / (float(frames) / timer.Val() / 100.0f) + << "] =====" << std::endl; + + //report objective function (accuracy, frame counts...) + std::cout << "-- " << (cross_validate?"CV":"TR") << p_obj_function->Report(); + std::cout << "T-fe: " << time_frontend << std::endl; + + return 0; ///finish OK + +} catch (std::exception& rExc) { + std::cerr << "Exception thrown" << std::endl; + std::cerr << rExc.what() << std::endl; + return 1; +} + |