average-inference.sh

#! /bin/bash
# Written by Mark Lee (github.com/minhoolee), August 9, 2018

set -e

# Define the directory that contains specific file structure
# Must be DEFECTS > SAMPLES > MODEL_IMAGES
test_dir=./data

# Define the model names
models=("DF_H"
        "DF_V"
        "NDF_H"
        "NDF_V")

# Input: name of directory holding samples and
#        name of directory that contains the model-named images
#
# Create files labelled as <DEFECT>_<SAMPLE>_<MODEL_NAME>.txt
run_models()
{
  defect=${1#$test_dir/}
  sample=${2##*/}

  # Loop through the models
  for (( i = 0; i < ${#models[@]}; ++i ))
  do
    model_name="${models[$i]}"
    predictions_file="$test_dir/$defect/$sample/${defect}_${sample}_${model_name}.txt"
    img_dir="$model_name.jpg"

    # Call darknet on each of the models for its image
    printf "\nRunning darknet model ${model_name} and saving predictions to $predictions_file\n\n"

    darknet classifier predict \
      "cfg/defect_${model_name}.data" \
      "cfg/densenet201_defect_${model_name}.cfg" \
      "backup/densenet201_defect_${model_name}.backup" \
      "${model_name}.jpg" >> $predictions_file
  done
}

# Input: name of directory holding samples and
#        name of directory that contains the model-named images
#
# Average predictions (line by line) from <DEFECT>_<SAMPLE>_<MODEL_NAME>.txt 
# files into a new file labelled as <DEFECT>_<SAMPLE>_avg_pred.txt
average_models()
{
  defect=${1#$test_dir/}
  sample=${2##*/}

  declare -a sum
  out_file="$test_dir/$defect/$sample/${defect}_${sample}_avg_preds.txt"

  # Loop through the models
  for (( i = 0; i < ${#models[@]}; ++i ))
  do
    model_name="${models[$i]}"
    predictions_file="$test_dir/$defect/$sample/${defect}_${sample}_${model_name}.txt"

    if ! [[ -s $predictions_file ]]
    then
      printf "\nERROR: No prediction file found: $predictions_file)\n"
      exit 64
    fi

    # Read lines from prediction file and save to pred array
    IFS=$'\r\n' GLOBIGNORE='*' command eval "pred=($(cat ${predictions_file} | sed 's/[^0-9.]//g'))"

    # Check if # of values in model predictions is not same as average
    if [[ $i > 0 && ${#pred[@]} != ${#sum[@]} ]]
    then
      printf "\nWARNING: # of predictions in ${predictions_file} is incorrect\n\n"
    fi

    # Loop through all the lines in the predictions file
    for (( line = 0; line < ${#pred[@]}; ++line ))
    do
      if [[ -z ${sum[line]} ]]
      then
        sum[$line]=0
      fi

      # Add prediction / number of models to the current average
      sum[$line]=`bc <<< "scale = 10; ${sum[line]} + \
        (${pred[line]}/${#models[@]})"`
    done
  done

  printf "\nCreating $out_file\n\n"
  printf "%s\n" "${sum[@]}" > $out_file
}

for defect in $test_dir/*
do
  for sample in "$defect"/*
  do
    run_models $defect $sample
    average_models $defect $sample
  done
done