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run_train_imagenet.sh
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run_train_imagenet.sh
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#!/bin/bash
DIR_PATH_NUM=1
GPU=0
DATE=`date +%m-%d-%T`
INIT_SIZE=128120 # 10%
BUDGET=64060 # 5%
SUBSET=$((BUDGET * 10))
DATA=imagenet
CLASSES=1000
APP=OO4AL # ['Random', 'LL4AL', 'OO4AL'] Please select an option.
Layer=1
for SEED in 0 1 2
do
argv=( )
argv+=( --save_name jobs/${DATA}__approach-${APP}/Init${INIT_SIZE}\_S${SUBSET}_B${BUDGET}/${METHOD}/Seed-${SEED} )
argv+=( --initial ${INIT_SIZE} )
argv+=( --selected ${BUDGET} )
argv+=( --sampling_num ${SUBSET} )
argv+=( --data_path ImageNet2012/ )
argv+=( --dataset ${DATA} )
argv+=( --class_num ${CLASSES} )
argv+=( --seed ${SEED} )
argv+=( --approach ${APP} )
argv+=( --layer ${Layer} )
argv+=( --epoch_num 100 )
argv+=( --cycle_num 5 )
argv+=( --workers 8 )
argv+=( --batch_size 256 )
argv+=( --epochl 60 )
argv+=( --wdecay 0.0001 )
argv+=( --milestones 30 60 90 )
argv+=( --lossnet_feature 56 28 14 7 )
echo OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=${GPU} python main.py "${argv[@]}"
echo ""
OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=${GPU} python main.py "${argv[@]}"
done