#!/bin/bash
  
  # run_resnet_1a.sh is a quite well-performing resnet.
  #  It includes a form of shrinkage that approximates l2 regularization.
  #  (c.f. --proportional-shrink).
  
  #  Definitely better:
  
  # local/nnet3/compare.sh exp/resnet1a_cifar10
  # System                 resnet1a_cifar10
  # final test accuracy:       0.9481
  # final train accuracy:        0.9992
  # final test objf:          -0.171369
  # final train objf:       -0.00980603
  # num-parameters:            1322730
  
  # local/nnet3/compare.sh exp/resnet1a_cifar100
  # System              resnet1a_cifar100
  # final test accuracy:        0.7478
  # final train accuracy:       0.9446
  # final test objf:           -0.899789
  # final train objf:          -0.22468
  # num-parameters:             1345860
  
  
  
  # Set -e here so that we catch if any executable fails immediately
  set -euo pipefail
  
  
  
  # training options
  stage=0
  train_stage=-10
  dataset=cifar10
  srand=0
  reporting_email=
  affix=1a
  
  
  # End configuration section.
  echo "$0 $@"  # Print the command line for logging
  
  . ./cmd.sh
  . ./path.sh
  . ./utils/parse_options.sh
  
  if ! cuda-compiled; then
    cat <<EOF && exit 1
  This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
  If you want to use GPUs (and have them), go to src/, and configure and make on a machine
  where "nvcc" is installed.
  EOF
  fi
  
  
  
  dir=exp/resnet${affix}_${dataset}
  
  egs=exp/${dataset}_egs2
  
  if [ ! -d $egs ]; then
    echo "$0: expected directory $egs to exist.  Run the get_egs.sh commands in the"
    echo "    run.sh before this script."
    exit 1
  fi
  
  # check that the expected files are in the egs directory.
  
  for f in $egs/egs.1.ark $egs/train_diagnostic.egs $egs/valid_diagnostic.egs $egs/combine.egs \
           $egs/info/feat_dim $egs/info/left_context $egs/info/right_context \
           $egs/info/output_dim; do
    if [ ! -e $f ]; then
      echo "$0: expected file $f to exist."
      exit 1;
    fi
  done
  
  
  mkdir -p $dir/log
  
  
  if [ $stage -le 1 ]; then
    mkdir -p $dir
    echo "$0: creating neural net configs using the xconfig parser";
  
    num_targets=$(cat $egs/info/output_dim)
  
    # Note: we hardcode in the CNN config that we are dealing with 32x3x color
    # images.
  
  
    nf1=48
    nf2=96
    nf3=256
    nb3=128
  
    common="required-time-offsets=0 height-offsets=-1,0,1"
    res_opts="bypass-source=batchnorm"
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=96 name=input
    conv-layer name=conv1 height-in=32 height-out=32 time-offsets=-1,0,1 required-time-offsets=0 height-offsets=-1,0,1 num-filters-out=$nf1
    res-block name=res2 num-filters=$nf1 height=32 time-period=1 $res_opts
    res-block name=res3 num-filters=$nf1 height=32 time-period=1 $res_opts
    conv-layer name=conv4 height-in=32 height-out=16 height-subsample-out=2 time-offsets=-1,0,1 $common num-filters-out=$nf2
    res-block name=res5 num-filters=$nf2 height=16 time-period=2 $res_opts
    res-block name=res6 num-filters=$nf2 height=16 time-period=2 $res_opts
    conv-layer name=conv7 height-in=16 height-out=8 height-subsample-out=2 time-offsets=-2,0,2 $common num-filters-out=$nf3
    res-block name=res8 num-filters=$nf3 num-bottleneck-filters=$nb3 height=8 time-period=4 $res_opts
    res-block name=res9 num-filters=$nf3 num-bottleneck-filters=$nb3 height=8 time-period=4 $res_opts
    res-block name=res10 num-filters=$nf3 num-bottleneck-filters=$nb3 height=8 time-period=4 $res_opts
    channel-average-layer name=channel-average input=Append(2,6,10,14,18,22,24,28) dim=$nf3
    output-layer name=output learning-rate-factor=0.1 dim=$num_targets
  EOF
    steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
  fi
  
  
  if [ $stage -le 2 ]; then
  
    steps/nnet3/train_raw_dnn.py --stage=$train_stage \
      --cmd="$train_cmd" \
      --image.augmentation-opts="--horizontal-flip-prob=0.5 --horizontal-shift=0.1 --vertical-shift=0.1 --num-channels=3" \
      --trainer.srand=$srand \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs=60 \
      --egs.frames-per-eg=1 \
      --trainer.optimization.num-jobs-initial=1 \
      --trainer.optimization.num-jobs-final=2 \
      --trainer.optimization.initial-effective-lrate=0.003 \
      --trainer.optimization.final-effective-lrate=0.0003 \
      --trainer.optimization.minibatch-size=256,128,64 \
      --trainer.optimization.proportional-shrink=50.0 \
      --trainer.shuffle-buffer-size=2000 \
      --egs.dir="$egs" \
      --use-gpu=true \
      --reporting.email="$reporting_email" \
      --dir=$dir  || exit 1;
  fi
  
  
  exit 0;