#!/bin/bash
  # Copyright 2013 Karel Vesely
  
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #  http://www.apache.org/licenses/LICENSE-2.0
  #
  # THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
  # WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
  # MERCHANTABLITY OR NON-INFRINGEMENT.
  # See the Apache 2 License for the specific language governing permissions and
  # limitations under the License.
  
  # To be run from ..
  #
  # Deep Belief Network pre-training by Contrastive Divergence (CD-1) algorithm.
  # The script can pre-train on plain features (ie. saved fMLLR features), 
  # or modified features (CMN, delta).
  # The script creates feature-transform in nnet format, which contains splice 
  # and shift+scale (zero mean and unit variance on DBN input).
  #
  # For special cases it is possible to use external feature-transform.
  # 
  
  # Begin configuration.
  #
  # nnet config
  nn_depth=6     #number of hidden layers
  hid_dim=2048   #number of units per layer
  # number of iterations
  rbm_iter=1            #number of pre-training epochs (Gaussian-Bernoulli RBM has 2x more)
  rbm_drop_data=0.0     #sample the training set, 1.0 drops all the data, 0.0 keeps all
  # pre-training opts
  rbm_lrate=0.4         #RBM learning rate
  rbm_lrate_low=0.01    #lower RBM learning rate (for Gaussian units)
  rbm_l2penalty=0.0002  #L2 penalty (increases RBM-mixing rate)
  # data processing config
  copy_feats=true    # resave the features randomized consecutively to tmpdir
  # feature config
  feature_transform= # Optionally reuse feature processing front-end (override splice,etc.)
  delta_order=       # Optionally use deltas on the input features
  apply_cmvn=false   # Optionally do CMVN of the input features
  norm_vars=false    # When apply_cmvn=true, this enables CVN
  splice=5           # Temporal splicing
  splice_step=1      # Stepsize of the splicing (1 is consecutive splice, 
                     # value 2 would do [ -10 -8 -6 -4 -2 0 2 4 6 8 10 ] splicing)
  # misc.
  verbose=1 # enable per-cache reports
  # gpu config
  use_gpu_id= # manually select GPU id to run on, (-1 disables GPU) 
  # End configuration.
  
  echo "$0 $@"  # Print the command line for logging
  
  [ -f path.sh ] && . ./path.sh;
  . parse_options.sh || exit 1;
  
  
  if [ $# != 2 ]; then
     echo "Usage: $0 <data> <exp-dir>"
     echo " e.g.: $0 data/train exp/rbm_pretrain"
     echo "main options (for others, see top of script file)"
     echo "  --config <config-file>           # config containing options"
     echo ""
     echo "  --nn-depth <N>                   # number of RBM layers"
     echo "  --hid-dim <N>                    # number of hidden units per layer"
     echo "  --rbm-iter <N>                   # number of CD-1 iterations per layer"
     echo "  --dbm-drop-data <float>          # probability of frame-dropping,"
     echo "                                   # can be used to subsample large datasets"
     echo "  --rbm-lrate <float>              # learning-rate for Bernoulli-Bernoulli RBMs"
     echo "  --rbm-lrate-low <float>          # learning-rate for Gaussian-Bernoulli RBM"
     echo ""
     echo "  --copy-feats <bool>              # copy features to /tmp, to accelerate training"
     echo "  --apply-cmvn <bool>              # normalize input features (opt.)"
     echo "    --norm-vars <bool>               # use variance normalization (opt.)"
     echo "  --splice <N>                     # splice +/-N frames of input features"
     exit 1;
  fi
  
  data=$1
  dir=$2
  
  
  for f in $data/feats.scp; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  echo "# INFO"
  echo "$0 : Pre-training Deep Belief Network as a stack of RBMs"
  printf "\t dir       : $dir 
  "
  printf "\t Train-set : $data 
  "
  
  [ -e $dir/${nn_depth}.dbn ] && echo "$0 Skipping, already have $dir/${nn_depth}.dbn" && exit 0
  
  mkdir -p $dir/log
  
  ###### PREPARE FEATURES ######
  echo
  echo "# PREPARING FEATURES"
  # shuffle the list
  echo "Preparing train/cv lists"
  cat $data/feats.scp | utils/shuffle_list.pl --srand ${seed:-777} > $dir/train.scp
  # print the list size
  wc -l $dir/train.scp
  
  #re-save the shuffled features, so they are stored sequentially on the disk in /tmp/
  if [ "$copy_feats" == "true" ]; then
    tmpdir=$(mktemp -d); mv $dir/train.scp $dir/train.scp_non_local
    utils/nnet/copy_feats.sh $dir/train.scp_non_local $tmpdir $dir/train.scp
    #remove data on exit...
    trap "echo \"Removing features tmpdir $tmpdir @ $(hostname)\"; rm -r $tmpdir" EXIT
  fi
  
  #create a 10k utt subset for global cmvn estimates
  head -n 10000 $dir/train.scp > $dir/train.scp.10k
  
  
  
  ###### PREPARE FEATURE PIPELINE ######
  
  #read the features
  feats="ark:copy-feats scp:$dir/train.scp ark:- |"
  
  #optionally add per-speaker CMVN
  if [ $apply_cmvn == "true" ]; then
    echo "Will use CMVN statistics : $data/cmvn.scp"
    [ ! -r $data/cmvn.scp ] && echo "Cannot find cmvn stats $data/cmvn.scp" && exit 1;
    cmvn="scp:$data/cmvn.scp"
    feats="$feats apply-cmvn --print-args=false --norm-vars=$norm_vars --utt2spk=ark:$data/utt2spk $cmvn ark:- ark:- |"
    # keep track of norm_vars option
    echo "$norm_vars" >$dir/norm_vars 
  else
    echo "apply_cmvn disabled (per speaker norm. on input features)"
  fi
  
  #optionally add deltas
  if [ "$delta_order" != "" ]; then
    feats="$feats add-deltas --delta-order=$delta_order ark:- ark:- |"
    echo "$delta_order" > $dir/delta_order
  fi
  
  #get feature dim
  echo -n "Getting feature dim : "
  feat_dim=$(feat-to-dim --print-args=false scp:$dir/train.scp -)
  echo $feat_dim
  
  
  # Now we will start building feature_transform which will 
  # be applied in CUDA to gain more speed.
  #
  # We will use 1GPU for both feature_transform and MLP training in one binary tool. 
  # This is against the kaldi spirit, but it is necessary, because on some sites a GPU 
  # cannot be shared accross by two or more processes (compute exclusive mode),
  # and we would like to use single GPU per training instance,
  # so that the grid resources can be used efficiently...
  
  
  if [ ! -z "$feature_transform" ]; then
    echo Using already prepared feature_transform: $feature_transform
    cp $feature_transform $dir/final.feature_transform
  else
    # Generate the splice transform
    echo "Using splice +/- $splice , step $splice_step"
    feature_transform=$dir/tr_splice$splice-$splice_step.nnet
    utils/nnet/gen_splice.py --fea-dim=$feat_dim --splice=$splice --splice-step=$splice_step > $feature_transform
  
    # Renormalize the MLP input to zero mean and unit variance
    feature_transform_old=$feature_transform
    feature_transform=${feature_transform%.nnet}_cmvn-g.nnet
    echo "Renormalizing MLP input features into $feature_transform"
    nnet-forward ${use_gpu_id:+ --use-gpu-id=$use_gpu_id} \
      $feature_transform_old "$(echo $feats | sed 's|train.scp|train.scp.10k|')" \
      ark:- 2>$dir/log/cmvn_glob_fwd.log |\
    compute-cmvn-stats ark:- - | cmvn-to-nnet - - |\
    nnet-concat --binary=false $feature_transform_old - $feature_transform
  
    # MAKE LINK TO THE FINAL feature_transform, so the other scripts will find it ######
    [ -f $dir/final.feature_transform ] && unlink $dir/final.feature_transform
    (cd $dir; ln -s $(basename $feature_transform) final.feature_transform )
  fi
  
  
  
  ###### GET THE DIMENSIONS ######
  num_fea=$(feat-to-dim --print-args=false "$feats nnet-forward --use-gpu-id=-1 $feature_transform ark:- ark:- |" - 2>/dev/null)
  num_hid=$hid_dim
  
  
  ###### PERFORM THE PRE-TRAINING ######
  for depth in $(seq 1 $nn_depth); do
    echo
    echo "# PRE-TRAINING RBM LAYER $depth"
    RBM=$dir/$depth.rbm
    [ -f $RBM ] && echo "RBM '$RBM' already trained, skipping." && continue
  
    #The first RBM needs special treatment, because of Gussian input nodes
    if [ "$depth" == "1" ]; then
      #This is Gaussian-Bernoulli RBM
      #initialize
      echo "Initializing '$RBM.init'"
      utils/nnet/gen_rbm_init.py --dim=${num_fea}:${num_hid} --gauss --vistype=gauss --hidtype=bern > $RBM.init || exit 1
      #pre-train
      echo "Pretraining '$RBM' (reduced lrate and 2x more iters)"
      rbm-train-cd1-frmshuff --learn-rate=$rbm_lrate_low --l2-penalty=$rbm_l2penalty \
        --num-iters=$((2*$rbm_iter)) --drop-data=$rbm_drop_data --verbose=$verbose \
        --feature-transform=$feature_transform \
        ${use_gpu_id:+ --use-gpu-id=$use_gpu_id} \
        $RBM.init "$feats" $RBM 2>$dir/log/rbm.$depth.log || exit 1
    else
      #This is Bernoulli-Bernoulli RBM
      #cmvn stats for init
      echo "Computing cmvn stats '$dir/$depth.cmvn' for RBM initialization"
      if [ ! -f $dir/$depth.cmvn ]; then 
        nnet-forward ${use_gpu_id:+ --use-gpu-id=$use_gpu_id} \
         "nnet-concat $feature_transform $dir/$((depth-1)).dbn - |" \
          "$(echo $feats | sed 's|train.scp|train.scp.10k|')" \
          ark:- 2>$dir/log/cmvn_fwd.$depth.log | \
        compute-cmvn-stats ark:- - 2>$dir/log/cmvn.$depth.log | \
        cmvn-to-nnet - $dir/$depth.cmvn || exit 1
      else
        echo compute-cmvn-stats already done, skipping.
      fi
      #initialize
      echo "Initializing '$RBM.init'"
      utils/nnet/gen_rbm_init.py --dim=${num_hid}:${num_hid} --gauss --vistype=bern --hidtype=bern --cmvn-nnet=$dir/$depth.cmvn > $RBM.init || exit 1
      #pre-train
      echo "Pretraining '$RBM'"
      rbm-train-cd1-frmshuff --learn-rate=$rbm_lrate --l2-penalty=$rbm_l2penalty \
        --num-iters=$rbm_iter --drop-data=$rbm_drop_data --verbose=$verbose \
        --feature-transform="nnet-concat $feature_transform $dir/$((depth-1)).dbn - |" \
        ${use_gpu_id:+ --use-gpu-id=$use_gpu_id} \
        $RBM.init "$feats" $RBM 2>$dir/log/rbm.$depth.log || exit 1
    fi
  
    #Create DBN stack
    if [ "$depth" == "1" ]; then
      rbm-convert-to-nnet --binary=true $RBM $dir/$depth.dbn
    else 
      rbm-convert-to-nnet --binary=true $RBM - | \
      nnet-concat $dir/$((depth-1)).dbn - $dir/$depth.dbn
    fi
  
  done
  
  echo
  echo "# REPORT"
  echo "# RBM pre-training progress (line per-layer)"
  grep progress $dir/log/rbm.*.log
  echo 
  
  echo "Pre-training finished."
  
  sleep 3
  exit 0