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1 files changed, 350 insertions, 0 deletions

diff --git a/src/TNormCu.cc b/src/TNormCu.cc
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+
+/***************************************************************************
+ *   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: TNormCu.cc 94 2011-12-08 10:59:03Z iveselyk $"
+
+#define MODULE_VERSION "1.0.0 "__TIME__" "__DATE__" "SVN_ID  
+
+
+
+/*** KaldiLib 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 "Nnet.h"
+
+/*** STL includes */
+#include <iostream>
+#include <sstream>
+#include <numeric>
+
+
+
+
+//////////////////////////////////////////////////////////////////////
+// DEFINES
+//
+
+#define SNAME "TNORM"
+
+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"
+" -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"
+"NATURALREADORDER PRINTCONFIG PRINTVERSION SCRIPT SOURCEMMF TARGETMMF TRACE\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 =
+    " -D n   PRINTCONFIG=TRUE"
+    " -H l   SOURCEMMF"
+    " -S l   SCRIPT"
+    " -T r   TRACE"
+    " -V n   PRINTVERSION=TRUE"
+    ;
+
+
+  UserInterface        ui;
+  FeatureRepository    features;
+  CuNetwork            network;
+  Network              network_cpu;
+  Timer                timer;
+
+ 
+  const char*                       p_script;
+  const char*                       p_source_mmf_file;
+  const char*                       p_targetmmf; 
+
+  int traceFlag;
+
+
+  // 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_targetmmf         = ui.GetStr(SNAME":TARGETMMF",     NULL);//< target for mean/variance
+
+  p_script            = ui.GetStr(SNAME":SCRIPT",         NULL);
+
+  traceFlag       = ui.GetInt(SNAME":TRACE",               0);
+  if(traceFlag&1) { 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 << "======= TNET 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++) {
+    features.AddFile(argv[args_parsed]);
+  }
+
+  //**************************************************************************
+  //**************************************************************************
+  // OPTION PARSING DONE .....................................................
+
+  //read the neural network
+  if(NULL != p_source_mmf_file) { 
+    if(CuDevice::Instantiate().IsPresent()) {
+      if(traceFlag&1) TraceLog(std::string("Reading GPU network: ")+p_source_mmf_file);
+      network.ReadNetwork(p_source_mmf_file);
+    } else {
+      if(traceFlag&1) TraceLog(std::string("Reading CPU network: ")+p_source_mmf_file);
+      network_cpu.ReadNetwork(p_source_mmf_file);
+    }
+  } else {
+    Error("Source MMF must be specified [-H]");
+  }
+
+
+
+
+  // initialize the feature repository 
+  features.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) {
+    features.AddFileList(p_script);
+  } else {
+    Warning("WARNING: The script file is missing [-S]");
+  }
+
+
+  
+  
+  //**********************************************************************
+  //**********************************************************************
+  // INITIALIZATION DONE .................................................
+  //
+  // Start training
+  timer.Start();
+  std::cout << "===== TNormCu STARTED =====" << std::endl;
+
+  int dim = CuDevice::Instantiate().IsPresent() ?
+                network.GetNOutputs() :
+                network_cpu.GetNOutputs();
+
+  Vector<double> first(dim); first.Set(0.0);
+  Vector<double> second(dim); second.Set(0.0);
+
+  unsigned long framesN = 0;
+ 
+  //progress
+  size_t cnt = 0;
+  size_t step = features.QueueSize() / 100;
+  if(step == 0) step = 1;
+ 
+  //**********************************************************************
+  //**********************************************************************
+  // MAIN LOOP
+
+  for(features.Rewind(); !features.EndOfList(); features.MoveNext()) {
+
+    Matrix<BaseFloat> feats_host,net_out;
+    Matrix<BaseFloat> feats_host_out;
+    CuMatrix<BaseFloat> feats;
+    CuMatrix<BaseFloat> feats_expanded;
+  
+    //get features 
+    features.ReadFullMatrix(feats_host);
+
+    if(CuDevice::Instantiate().IsPresent()) {
+      //propagate 
+      feats.CopyFrom(feats_host);
+      network.Propagate(feats,feats_expanded);
+      
+      //trim the xxx_frm_ext
+      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);
+      feats_trim.CopyTo(feats_host_out);
+    } else {
+      //propagate
+      network_cpu.Propagate(feats_host,net_out);
+      //trim the xxx_frm_ext
+      feats_host_out.Init(net_out.Rows()-start_frm_ext-end_frm_ext,net_out.Cols());
+      memcpy(feats_host_out.pData(),net_out.pRowData(start_frm_ext),feats_host_out.MSize());
+    }
+
+    //accumulate first/second order statistics
+    for(size_t m=0; m<feats_host_out.Rows(); m++) {
+      for(size_t n=0; n<feats_host_out.Cols(); n++) {
+        BaseFloat val = feats_host_out(m,n);
+        first[n] += val; 
+        second[n] += val*val;
+
+        if(isnan(first[n])||isnan(second[n])||
+           isinf(first[n])||isinf(second[n])) 
+        {
+          std::ostringstream oss;
+          oss << "nan/inf in accumulators\n"
+              << "first:" << first << "\n"
+              << "second:" << second << "\n"
+              << "frames:" << framesN << "\n"
+              << "utterance:" << features.Current().Logical() << "\n"
+              << "feats_host: " << feats_host << "\n"
+              << "feats_host_out: " << feats_host_out << "\n";
+          Error(oss.str());
+        }
+      }
+    }
+
+    
+
+    framesN += feats_host.Rows();
+    
+    //progress 
+    if((cnt++ % step) == 0) std::cout << 100 * cnt / features.QueueSize() << "%, " << std::flush;
+  }
+
+  //**********************************************************************
+  //**********************************************************************
+  // ACCUMULATING FINISHED .................................................
+  //
+
+
+  //get the mean/variance vectors
+  Vector<double> mean(first);
+  mean.Scale(1.0/framesN);
+  Vector<double> variance(second);
+  variance.Scale(1.0/framesN);
+  for(size_t i=0; i<mean.Dim(); i++) {
+    variance[i] -= mean[i]*mean[i];
+  }
+
+  //get the mean normalization biase vector, 
+  //use negative mean vector
+  Vector<double> bias(mean);
+  bias.Scale(-1.0);
+
+  //get the variance normalization window vector, 
+  //inverse of square root of variance
+  Vector<double> window(variance);
+  for(size_t i=0; i<window.Dim(); i++) {
+    window[i] = 1.0/sqrt(window[i]);
+  }
+
+  //store the normalization network
+  std::ofstream os(p_targetmmf);
+  if(!os.good()) Error(std::string("Cannot open file for writing: ")+p_targetmmf);
+
+  dim = mean.Dim();
+  os << "<bias> " << dim << " " << dim << "\n"
+     << bias << "\n\n"
+     << "<window> " << dim << " " << dim << "\n"
+     << window << "\n\n";
+
+  os.close();
+
+  timer.End();
+  std::cout << "\n\n===== TNormCu FINISHED ( " << timer.Val() << "s ) "
+            << "[FPS:" << framesN / timer.Val() 
+            << ",RT:" << 1.0f / (framesN / timer.Val() / 100.0f)
+            << "] =====" << std::endl;
+
+  std::cout << "frames: " << framesN 
+            << ", max_bias: " << bias.Max()
+            << ", max_window: " << window.Max()
+            << ", min_window: " << window.Min()
+            << "\n";
+  
+  return  0; ///finish OK
+
+} catch (std::exception& rExc) {
+  std::cerr << "Exception thrown" << std::endl;
+  std::cerr << rExc.what() << std::endl;
+  return  1;
+}
+