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#!/bin/bash
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
resolution=$2 # 64,128,256
dataset=$1 #'imagenet', 'imagenet_lt', 'coco', [a transfer dataset, such as 'cityscapes']
out_path=''
path_imnet=''
path_swav='swav_800ep_pretrain.pth.tar'
path_classifier_lt='resnet50_uniform_e90.pth'
##################
#### ImageNet ####
##################
if [ $dataset = 'imagenet' ]; then
python data_utils/make_hdf5.py --resolution $resolution --split 'train' --data_root $path_imnet --out_path $out_path --feature_extractor 'classification' --feature_augmentation
python data_utils/make_hdf5.py --resolution $resolution --split 'train' --data_root $path_imnet --out_path $out_path --save_features_only --feature_extractor 'selfsupervised' --feature_augmentation --pretrained_model_path $path_swav
python data_utils/make_hdf5.py --resolution $resolution --split 'val' --data_root $path_imnet --out_path $out_path --save_images_only
## Calculate inception moments
for split in 'train' 'val'; do
python data_utils/calculate_inception_moments.py --resolution $resolution --split 'train' --data_root $out_path --load_in_mem --out_path $out_path
done
# Compute NNs
python data_utils/make_hdf5_nns.py --resolution $resolution --split 'train' --feature_extractor 'classification' --data_root $out_path --out_path $out_path --k_nn 50
python data_utils/make_hdf5_nns.py --resolution $resolution --split 'train' --feature_extractor 'selfsupervised' --data_root $out_path --out_path $out_path --k_nn 50
elif [ $dataset = 'imagenet_lt' ]; then
python data_utils/make_hdf5.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'train' --data_root $path_imnet --out_path $out_path --feature_extractor 'classification' --feature_augmentation --pretrained_model_path $path_classifier_lt
python data_utils/make_hdf5.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'val' --data_root $path_imnet --out_path $out_path --save_images_only
# Calculate inception moments
python data_utils/calculate_inception_moments.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'train' --data_root $out_path --out_path $out_path
python data_utils/calculate_inception_moments.py --resolution $resolution --split 'val' --data_root $out_path --out_path $out_path --stratified_moments
# Compute NNs
python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'train' --feature_extractor 'classification' --data_root $out_path --out_path $out_path --k_nn 5
elif [ $dataset = 'coco' ]; then
path_split=("train" "val")
split=("train" "test")
for i in "${!path_split[@]}"; do
coco_data_path='COCO/022719/'${path_split[i]}'2017'
coco_instances_path='datasets/coco/annotations/instances_'${path_split[i]}'2017.json'
coco_stuff_path='datasets/coco/annotations/stuff_'${path_split[i]}'2017.json'
python data_utils/make_hdf5.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --data_root $coco_data_path --instance_json $coco_instances_path --stuff_json $coco_stuff_path --out_path $out_path --feature_extractor 'selfsupervised' --feature_augmentation --pretrained_model_path $path_swav
python data_utils/make_hdf5.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --data_root $coco_data_path --instance_json $coco_instances_path --stuff_json $coco_stuff_path --out_path $out_path --feature_extractor 'classification' --feature_augmentation
# Calculate inception moments
python data_utils/calculate_inception_moments.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --data_root $out_path --load_in_mem --out_path $out_path
# Compute NNs
python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --feature_extractor 'selfsupervised' --data_root $out_path --out_path $out_path --k_nn 5
python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset 'coco' --split ${split[i]} --feature_extractor 'classification' --data_root $out_path --out_path $out_path --k_nn 5
done
# Transfer datasets
else
python data_utils/make_hdf5.py --resolution $resolution --which_dataset $dataset --split 'train' --data_root $3 --feature_extractor 'classification' --out_path $out_path
# Compute NNs
python data_utils/make_hdf5.py --resolution $resolution --which_dataset $dataset --split 'train' --data_root $3 --feature_extractor 'selfsupervised' --pretrained_model_path $path_swav --save_features_only --out_path $out_path
# Compute NNs
# Compute NNs
python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset $dataset --split 'train' --feature_extractor 'classification' --data_root $out_path --out_path $out_path --k_nn 5
python data_utils/make_hdf5_nns.py --resolution $resolution --which_dataset $dataset --split 'train' --feature_extractor 'selfsupervised' --data_root $out_path --out_path $out_path --k_nn 5
fi
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