52 lines
2.2 KiB
Bash
52 lines
2.2 KiB
Bash
#!/bin/bash
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# bash ./exps/NATS-algos/run-all.sh mul
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# bash ./exps/NATS-algos/run-all.sh ws
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set -e
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echo script name: $0
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echo $# arguments
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if [ "$#" -ne 1 ] ;then
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echo "Input illegal number of parameters " $#
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echo "Need 1 parameters for type of algorithms."
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exit 1
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fi
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alg_type=$1
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if [ "$alg_type" == "mul" ]; then
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# datasets="cifar10 cifar100 ImageNet16-120"
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run_four_algorithms(){
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dataset=$1
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search_space=$2
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time_budget=$3
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python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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}
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# The topology search space
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run_four_algorithms "cifar10" "tss" "20000"
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run_four_algorithms "cifar100" "tss" "40000"
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run_four_algorithms "ImageNet16-120" "tss" "120000"
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# The size search space
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run_four_algorithms "cifar10" "sss" "20000"
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run_four_algorithms "cifar100" "sss" "40000"
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run_four_algorithms "ImageNet16-120" "sss" "60000"
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# python exps/experimental/vis-bench-algos.py --search_space tss
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# python exps/experimental/vis-bench-algos.py --search_space sss
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else
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seeds="777 888 999"
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algos="darts-v1 darts-v2 gdas setn random enas"
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epoch=200
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for seed in ${seeds}
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do
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for alg in ${algos}
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do
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python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo ${alg} --rand_seed ${seed} --overwite_epochs ${epoch}
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python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo ${alg} --rand_seed ${seed} --overwite_epochs ${epoch}
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python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo ${alg} --rand_seed ${seed} --overwite_epochs ${epoch}
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done
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done
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fi
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