#!/usr/bin/env perl
  use warnings; #sed replacement for -w perl parameter
  # Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
  
  # 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.
  
  
  # This script takes three command-line arguments.
  # The first is a log-file such as exp/tri4b_nnet/log/combine.10.log,
  # which is the output of nnet-combine.  The second is a file such
  # as exp/tri4b_nnet/11.tmp.mdl, i.e. a model file, for which we will
  # update the learning rates; the third is the output nnet file e.g.
  # exp/tri4b_nnet/11.mdl
  
  # This script assumes that the "combine" script is called as:
  # nnet-combine <old-model> <new-model-1> <new-model-2> ... <new-model-n> <validation-examples> <output-model>.
  # It gets from the logging output a line like this:
  # LOG (nnet-combine:CombineNnets():combine-nnet.cc:184) Combining nnets, validation objf per frame changed from -1.43424 to -1.42067, scale factors are  [ 0.727508 0.79889 0.299533 0.137696 -0.0479123 0.210445 0.0195638 0.123843 0.167453 0.0193894 -0.0128672 0.178384 0.0516549 0.0958205 0.125495 ]
  # [in this case the 1st 3 numbers correspond to the <old-model> ] and for each
  # updatable layer, it works out the total weight on the new models.
  # It interprets this as being (for each layer) a step length along
  # the path old-model -> new-model.
  # Basically, we change the learning rate by a factor equal to this step length,
  # subject to limits on the change  [by default limit to halving/doubling].
  # It's fairly obvious why we would want do do this.
  
  # These options can be useful if we want to splice the input
  # features across time.
  $sources_to_exclude = 1; # may make this configurable later.
  $min_learning_rate_factor = 0.5;
  $max_learning_rate_factor = 2.0;
  $min_learning_rate = 0.0001; # Put a floor because if too small,
    # the changes become zero due to roundoff.
  
  if (@ARGV > 0) {
    for ($x = 1; $x < 10; $x++) {
      if ($ARGV[0] eq "--min-learning-rate-factor") {
        $min_learning_rate_factor = $ARGV[1];
        shift; shift;
      }
      if ($ARGV[0] eq "--max-learning-rate-factor") {
        $max_learning_rate_factor = $ARGV[1];
        shift; shift;
      }
      if ($ARGV[0] eq "--min-learning-rate") {
        $min_learning_rate = $ARGV[1];
        shift; shift;
      }
    }
  }
  
  
  if (@ARGV != 3) {
    print STDERR "Usage: update_learning_rates.pl [options] <log-file-for-nnet-combine> <nnet-in> <nnet-out>
  Options:
     --min-learning-rate-factor       #  minimum factor to change learning rate by (default: 0.5)
     --max-learning-rate-factor       #  maximum factor to change learning rate by (default: 2.0)
  ";
     exit(1);
  }
  
  ($combine_log, $nnet_in, $nnet_out) = @ARGV;
  
  open(L, "<$combine_log") || die "Opening log file \"$combine_log\"";
  
  
  while(<L>) {
    if (m/Objective functions for the source neural nets are\s+\[(.+)\]/) {
      ## a line like:
      ##  LOG (nnet-combine:GetInitialScaleParams():combine-nnet.cc:66) Objective functions for the source neural nets are  [ -1.37002 -1.52115 -1.52103 -1.50189 -1.51912 ]
      @A = split(" ", $1);
      $num_sources = @A; # number of source neural nets (dimension of @A); 5 in this case.
    }
    ## a line like:
    ## LOG (nnet-combine:CombineNnets():combine-nnet.cc:184) Combining nnets, validation objf per frame changed from -1.37002 to -1.36574, scale factors are  [ 0.819379 0.696122 0.458798 0.040513 -0.0448875 0.171431 0.0274615 0.139143 0.133846 0.0372585 0.114193 0.17944 0.0491838 0.0668778 0.0328936 ]
    if (m/Combining nnets.+scale factors are\s+\[(.+)\]/) {
      @scale_factors = split(" ", $1);
    }
  }
  
  if (!defined $num_sources) {
    die "Log file $combine_log did not have expected format: no line with \"Objective functions\"
  ";
  }
  if (!defined @scale_factors) {
    die "Log file $combine_log did not have expected format: no line with \"Combining nnets\"
  ";
  }
  
  
  $num_scales = @scale_factors; # length of the array.
  if ($num_scales % $num_sources != 0) {
    die "Error interpreting log file $combine_log: $num_sources does not divide $num_scales
  ";
  }
  close(L);
  
  open(P, "nnet-am-info $nnet_in |") || die "Opening pipe from nnet-am-info";
  @learning_rates = ();
  while(<P>) {
    if (m/learning rate = ([^,]+),/) {
      push @learning_rates, $1;
    }
  }
  close(P);
  
  $num_layers = $num_scales / $num_sources;
  
  $num_info_learning_rates = @learning_rates;
  if ($num_layers != $num_info_learning_rates) {
    die "From log file we expect there to be $num_layers updatable components, but from the output of nnet-am-info we saw $num_info_learning_rates";
  }
  
  for ($layer = 0; $layer < $num_layers; $layer++) {
    # getting the sum of the weights for this layer from all the non-excluded sources.
    $sum = 0.0;
    for ($source = $sources_to_exclude; $source < $num_sources; $source++) {
      $index = ($source * $num_layers) + $layer;
      $sum += $scale_factors[$index];
    }
    $learning_rate_factor = $sum;
    if ($learning_rate_factor > $max_learning_rate_factor) { $learning_rate_factor = $max_learning_rate_factor; }
    if ($learning_rate_factor < $min_learning_rate_factor) { $learning_rate_factor = $min_learning_rate_factor; }
    $old_learning_rate = $learning_rates[$layer];
    $new_learning_rate = $old_learning_rate * $learning_rate_factor;
    if ($new_learning_rate < $min_learning_rate) { $new_learning_rate = $min_learning_rate; }
    print STDERR "For layer $layer, sum of weights of non-excluded sources is $sum, learning-rate factor is $learning_rate_factor
  ";
    $learning_rates[$layer] = $new_learning_rate;
  }
  
  $lrates_string=join(":", @learning_rates);
  
  $ret = system("nnet-am-copy --learning-rates=$lrates_string $nnet_in $nnet_out");
  
  exit($ret != 0);