#!/bin/bash
  
  # run_cnn_aug_1b is the same as run_cnn_1e but with data augmentation.
  
  # accuracy is 0.857, vs. 0.83 for the un-augmented baseline.
  
  # exp/cnn_aug_1b_cifar10: num-iters=60 nj=1..2 num-params=2.2M dim=96->10 combine=-0.40->-0.38 loglike:train/valid[39,59,final]=(-0.35,-0.26,-0.26/-0.47,-0.42,-0.42) accuracy:train/valid[39,59,final]=(0.88,0.91,0.91/0.84,0.86,0.86)
  
  # grep Overall exp/cnn_aug_1b_cifar10/log/compute_prob_valid.final.log  | grep acc
  # LOG (nnet3-compute-prob[5.1]:PrintTotalStats():nnet-diagnostics.cc:165) Overall accuracy for 'output' is 0.8567 per frame, over 10000 frames.#
  
  
  # 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=_aug_1b
  
  
  # 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/cnn${affix}_${dataset}
  
  egs=exp/${dataset}_egs
  
  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.
  
    common1="required-time-offsets=0 height-offsets=-1,0,1 num-filters-out=32"
    common2="required-time-offsets=0 height-offsets=-1,0,1 num-filters-out=64"
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=96 name=input
    conv-relu-batchnorm-layer name=cnn1 height-in=32 height-out=32 time-offsets=-1,0,1 $common1
    conv-relu-batchnorm-dropout-layer name=cnn2 height-in=32 height-out=16 time-offsets=-1,0,1 dropout-proportion=0.25 $common1 height-subsample-out=2
    conv-relu-batchnorm-layer name=cnn3 height-in=16 height-out=16 time-offsets=-2,0,2 $common2
    conv-relu-batchnorm-dropout-layer name=cnn4 height-in=16 height-out=8 time-offsets=-2,0,2 dropout-proportion=0.25 $common2 height-subsample-out=2
    conv-relu-batchnorm-layer name=cnn5 height-in=8 height-out=8 time-offsets=-4,0,4 $common2
    relu-dropout-layer name=fully_connected1 input=Append(2,6,10,14,18,22,26,30) dropout-proportion=0.5 dim=512
    output-layer name=output dim=$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=$srand \
      --trainer.max-param-change=2.0 \
      --trainer.num-epochs=30 \
      --egs.frames-per-eg=1 \
      --trainer.optimization.num-jobs-initial=1 \
      --trainer.optimization.num-jobs-final=2 \
      --trainer.optimization.initial-effective-lrate=0.003 \
      --trainer.optimization.final-effective-lrate=0.0003 \
      --trainer.optimization.minibatch-size=256,128,64 \
      --trainer.shuffle-buffer-size=2000 \
      --egs.dir="$egs" \
      --use-gpu=true \
      --reporting.email="$reporting_email" \
      --dir=$dir  || exit 1;
  fi
  
  
  exit 0;