chore: import upstream snapshot with attribution

tools/tensorflow-quantization/examples/data/imagenet_data_setup.sh

@@ -0,0 +1,44 @@
+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
+# ############################################################################################