ASL-MoViNet-T5-translator / official /vision /data /process_coco_few_shot.sh
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#!/bin/bash
#
# Processes the COCO few-shot benchmark into TFRecord files. Requires `wget`.
tmp_dir=$(mktemp -d -t coco-XXXXXXXXXX)
base_image_dir="/tmp/coco_images"
output_dir="/tmp/coco_few_shot"
while getopts ":i:o:" o; do
case "${o}" in
o) output_dir=${OPTARG} ;;
i) base_image_dir=${OPTARG} ;;
*) echo "Usage: ${0} [-i <base_image_dir>] [-o <output_dir>]" 1>&2; exit 1 ;;
esac
done
cocosplit_url="dl.yf.io/fs-det/datasets/cocosplit"
wget --recursive --no-parent -q --show-progress --progress=bar:force:noscroll \
-P "${tmp_dir}" -A "trainvalno5k.json,5k.json,*1shot*.json,*3shot*.json,*5shot*.json,*10shot*.json,*30shot*.json" \
"http://${cocosplit_url}/"
mv "${tmp_dir}/${cocosplit_url}/"* "${tmp_dir}"
rm -rf "${tmp_dir}/${cocosplit_url}/"
python process_coco_few_shot_json_files.py \
--logtostderr --workdir="${tmp_dir}"
for seed in {0..9}; do
for shots in 1 3 5 10 30; do
python create_coco_tf_record.py \
--logtostderr \
--image_dir="${base_image_dir}/train2014" \
--image_dir="${base_image_dir}/val2014" \
--image_info_file="${tmp_dir}/${shots}shot_seed${seed}.json" \
--object_annotations_file="${tmp_dir}/${shots}shot_seed${seed}.json" \
--caption_annotations_file="" \
--output_file_prefix="${output_dir}/${shots}shot_seed${seed}" \
--num_shards=4
done
done
python create_coco_tf_record.py \
--logtostderr \
--image_dir="${base_image_dir}/train2014" \
--image_dir="${base_image_dir}/val2014" \
--image_info_file="${tmp_dir}/datasplit/5k.json" \
--object_annotations_file="${tmp_dir}/datasplit/5k.json" \
--caption_annotations_file="" \
--output_file_prefix="${output_dir}/5k" \
--num_shards=10
python create_coco_tf_record.py \
--logtostderr \
--image_dir="${base_image_dir}/train2014" \
--image_dir="${base_image_dir}/val2014" \
--image_info_file="${tmp_dir}/datasplit/trainvalno5k_base.json" \
--object_annotations_file="${tmp_dir}/datasplit/trainvalno5k_base.json" \
--caption_annotations_file="" \
--output_file_prefix="${output_dir}/trainvalno5k_base" \
--num_shards=200
python create_coco_tf_record.py \
--logtostderr \
--image_dir="${base_image_dir}/train2014" \
--image_dir="${base_image_dir}/val2014" \
--image_info_file="${tmp_dir}/datasplit/5k_base.json" \
--object_annotations_file="${tmp_dir}/datasplit/5k_base.json" \
--caption_annotations_file="" \
--output_file_prefix="${output_dir}/5k_base" \
--num_shards=10
rm -rf "${tmp_dir}"