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#!/bin/bash |
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resolution=$2 |
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dataset=$1 |
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out_path='' |
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path_imnet='' |
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path_swav='swav_800ep_pretrain.pth.tar' |
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path_classifier_lt='resnet50_uniform_e90.pth' |
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if [ $dataset = 'imagenet' ]; then |
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python data_utils/make_hdf5.py --resolution $resolution --split 'train' --data_root $path_imnet --out_path $out_path --feature_extractor 'classification' --feature_augmentation |
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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 |
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python data_utils/make_hdf5.py --resolution $resolution --split 'val' --data_root $path_imnet --out_path $out_path --save_images_only |
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for split in 'train' 'val'; do |
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python data_utils/calculate_inception_moments.py --resolution $resolution --split 'train' --data_root $out_path --load_in_mem --out_path $out_path |
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done |
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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 |
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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 |
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elif [ $dataset = 'imagenet_lt' ]; then |
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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 |
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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 |
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python data_utils/calculate_inception_moments.py --resolution $resolution --which_dataset 'imagenet_lt' --split 'train' --data_root $out_path --out_path $out_path |
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python data_utils/calculate_inception_moments.py --resolution $resolution --split 'val' --data_root $out_path --out_path $out_path --stratified_moments |
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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 |
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elif [ $dataset = 'coco' ]; then |
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path_split=("train" "val") |
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split=("train" "test") |
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for i in "${!path_split[@]}"; do |
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coco_data_path='COCO/022719/'${path_split[i]}'2017' |
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coco_instances_path='datasets/coco/annotations/instances_'${path_split[i]}'2017.json' |
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coco_stuff_path='datasets/coco/annotations/stuff_'${path_split[i]}'2017.json' |
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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 |
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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 |
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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 |
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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 |
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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 |
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done |
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else |
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python data_utils/make_hdf5.py --resolution $resolution --which_dataset $dataset --split 'train' --data_root $3 --feature_extractor 'classification' --out_path $out_path |
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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 |
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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 |
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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 |
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fi |
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