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Diffstat (limited to 'src/TRbmCu.cc')
| -rw-r--r-- | src/TRbmCu.cc | 396 |
1 files changed, 396 insertions, 0 deletions
diff --git a/src/TRbmCu.cc b/src/TRbmCu.cc new file mode 100644 index 0000000..b2d5ea8 --- /dev/null +++ b/src/TRbmCu.cc @@ -0,0 +1,396 @@ + +/*************************************************************************** + * 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: 2011-12-08 11:59:03 +0100 (Thu, 08 Dec 2011) $" +#define SVN_AUTHOR "$Author: iveselyk $" +#define SVN_REVISION "$Revision: 94 $" +#define SVN_ID "$Id: TRbmCu.cc 94 2011-12-08 10:59:03Z iveselyk $" + +#define MODULE_VERSION "1.0.0 "__TIME__" "__DATE__" "SVN_ID + + + + + +/*** TNetLib includes */ +#include "Error.h" +#include "Timer.h" +#include "Features.h" +#include "Common.h" +#include "UserInterface.h" +#include "Timer.h" + +/*** TNet includes */ +#include "cuNetwork.h" +#include "cuRbm.h" +#include "cuCache.h" +#include "cuObjectiveFunction.h" +#include "curand.h" + +/*** STL includes */ +#include <iostream> +#include <sstream> +#include <numeric> + + + + +////////////////////////////////////////////////////////////////////// +// DEFINES +// + +#define SNAME "TRBM" + +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" +" Option Default\n\n" +" -n f Set learning rate to f 0.06\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" +" -S file Set script file None\n" +" -T N Set trace flags to N 0\n" +" -V Print version information Off\n" +"\n" +"FEATURETRANSFORM LEARNINGRATE MOMENTUM NATURALREADORDER PRINTCONFIG PRINTVERSION SCRIPT SOURCEMMF TARGETMMF 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 = + " -n r LEARNINGRATE" + " -D n PRINTCONFIG=TRUE" + " -H l SOURCEMMF" + " -S l SCRIPT" + " -T r TRACE" + " -V n PRINTVERSION=TRUE" + ; + + + UserInterface ui; + FeatureRepository feature_repo; + CuNetwork network; + CuNetwork transform_network; + CuMeanSquareError mse; + Timer timer; + Timer timer_frontend; + double time_frontend = 0.0; + + + const char* p_script; + BaseFloat learning_rate; + BaseFloat momentum; + BaseFloat weightcost; + + const char* p_source_mmf_file; + const char* p_input_transform; + + const char* p_targetmmf; + + int bunch_size; + int cache_size; + bool randomize; + long int seed; + + 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); + learning_rate = ui.GetFlt(SNAME":LEARNINGRATE" , 0.10f); + momentum = ui.GetFlt(SNAME":MOMENTUM" , 0.50f); + weightcost = ui.GetFlt(SNAME":WEIGHTCOST" , 0.0002f); + + + bunch_size = ui.GetInt(SNAME":BUNCHSIZE", 256); + cache_size = ui.GetInt(SNAME":CACHESIZE", 12800); + randomize = ui.GetBool(SNAME":RANDOMIZE", true); + + //cannot get long int + seed = ui.GetInt(SNAME":SEED", 0); + + trace = ui.GetInt(SNAME":TRACE", 0); + if(trace&4) { CuDevice::Instantiate().Verbose(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 << "======= TRbmCu v"MODULE_VERSION" xvesel39 =======" << 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]"); + } + //extract the RBM from the network + if(network.Layers() != 1) { + Error(std::string("Number of layers must be 1")+p_source_mmf_file); + } + if(network.Layer(0).GetType() != CuComponent::RBM && network.Layer(0).GetType() != CuComponent::RBM_SPARSE) { + Error(std::string("Layer must be RBM")+p_source_mmf_file); + } + CuRbmBase& rbm = dynamic_cast<CuRbmBase&>(network.Layer(0)); + + // 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]"); + } + feature_repo.Trace(trace); + + //set the learnrate, momentum, weightcost + rbm.LearnRate(learning_rate); + rbm.Momentum(momentum); + rbm.Weightcost(weightcost); + + //seed the random number generator + if(seed == 0) { + struct timeval tv; + if (gettimeofday(&tv, 0) == -1) { + assert(0 && "gettimeofday does not work."); + exit(-1); + } + seed = (int)(tv.tv_sec) + (int)tv.tv_usec; + } + srand48(seed); + + //initialize the matrix random number generator + CuRand<BaseFloat> cu_rand(bunch_size,rbm.GetNOutputs()); + + + + //********************************************************************** + //********************************************************************** + // INITIALIZATION DONE ................................................. + // + // Start training + timer.Start(); + std::cout << "===== TRbmCu TRAINING STARTED =====" << std::endl; + std::cout << "learning rate: " << learning_rate + << " momentum: " << momentum + << " weightcost: " << weightcost + << std::endl; + std::cout << "Using seed: " << seed << "\n"; + + + CuCache cache; + cache.Init(cache_size,bunch_size); + cache.Trace(trace); + feature_repo.Rewind(); + + //********************************************************************** + //********************************************************************** + // MAIN LOOP + // + CuMatrix<BaseFloat> pos_vis, pos_hid, neg_vis, neg_hid; + CuMatrix<BaseFloat> dummy_labs, dummy_err; + while(!feature_repo.EndOfList()) { + timer_frontend.Start(); + //fill cache + while(!cache.Full() && !feature_repo.EndOfList()) { + Matrix<BaseFloat> feats_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; + CuMatrix<BaseFloat> feats_trim(rows,feats_expanded.Cols()); + feats_trim.CopyRows(rows,start_frm_ext,feats_expanded,0); + + //fake the labels!!! + CuMatrix<BaseFloat> labs_cu(feats_trim.Rows(),1); + + //add to cache + cache.AddData(feats_trim,labs_cu); + + feature_repo.MoveNext(); + } + timer_frontend.End(); time_frontend += timer_frontend.Val(); + + if(randomize) { + //randomize the cache + cache.Randomize(); + } + + while(!cache.Empty()) { + //get training data + cache.GetBunch(pos_vis,dummy_labs); + + //forward pass + rbm.Propagate(pos_vis,pos_hid); + + //change the hidden values so we can generate negative example + if(rbm.HidType() == CuRbmBase::BERNOULLI) { + cu_rand.BinarizeProbs(pos_hid,neg_hid); + } else { + neg_hid.CopyFrom(pos_hid); + cu_rand.AddGaussNoise(neg_hid); + } + + //reconstruct pass + rbm.Reconstruct(neg_hid,neg_vis); + + //forward pass + rbm.Propagate(neg_vis, neg_hid); + + //update the weioghts + rbm.RbmUpdate(pos_vis, pos_hid, neg_vis, neg_hid); + + //evalueate mean square error + mse.Evaluate(neg_vis,pos_vis,dummy_err); + + if(trace&2) std::cout << "." << std::flush; + } + //check the NaN/inf + pos_hid.CheckData(); + } + + + + //********************************************************************** + //********************************************************************** + // TRAINING FINISHED ................................................. + // + // Let's store the network, report the log + + if(trace&1) TraceLog("Training finished"); + + //write the network + if (NULL != p_targetmmf) { + if(trace&1) TraceLog(std::string("Writing network: ")+p_targetmmf); + network.WriteNetwork(p_targetmmf); + } else { + Error("missing argument --TARGETMMF"); + } + + timer.End(); + std::cout << "===== TRbmCu FINISHED ( " << timer.Val() << "s ) " + << "[FPS:" << mse.GetFrames() / timer.Val() + << ",RT:" << 1.0f / (mse.GetFrames() / timer.Val() / 100.0f) + << "] =====" << std::endl; + + //report objective function (accuracy, frame counts...) + std::cout << mse.Report(); + + if(trace &4) { + std::cout << "\n== PROFILE ==\nT-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; +} |