tools/tensorflow-quantization/examples/data/imagenet_data_setup.sh
export IMAGENET_HOME=/media/Data/imagenet_data
# Setup folders
mkdir -p $IMAGENET_HOME/validation
mkdir -p $IMAGENET_HOME/train

# ###### Modification 1: set .tar files path to $IMAGENET_HOME #############
# Extract validation and training
tar xf $IMAGENET_HOME/ILSVRC2012_img_val.tar -C $IMAGENET_HOME/validation
tar xf $IMAGENET_HOME/ILSVRC2012_img_train.tar -C $IMAGENET_HOME/train
# ##########################################################################

# Extract and then delete individual training tar files This can be pasted
# directly into a bash command-line or create a file and execute.
cd $IMAGENET_HOME/train

for f in *.tar; do
  d=`basename $f .tar`
  mkdir $d
  tar xf $f -C $d
done

cd $IMAGENET_HOME # Move back to the base folder

# [Optional] Delete tar files if desired as they are not needed
rm $IMAGENET_HOME/train/*.tar

# ###### Modification 2: Updated deprecated link #############
# Download labels file.
wget -O $IMAGENET_HOME/synset_labels.txt \
https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_2012_validation_synset_labels.txt
# ############################################################

# Process the files. Remember to get the script from github first. The TFRecords
# will end up in the --local_scratch_dir. To upload to gcs with this method
# leave off `nogcs_upload` and provide gcs flags for project and output_path.
python imagenet_to_gcs.py \
  --raw_data_dir=$IMAGENET_HOME \
  --local_scratch_dir=$IMAGENET_HOME/tf_records \
  --nogcs_upload

# ######## Modification 3: move train and validation files to root dir #######################
mv $IMAGENET_HOME/tf_records/train* $IMAGENET_HOME/tf_records
mv $IMAGENET_HOME/tf_records/validation* $IMAGENET_HOME/tf_records
# ############################################################################################
