iicd/xautodl
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update time_budget for NATS (algos)

Browse Source
This commit is contained in:
D-X-Y 2020-11-26 14:43:28 +08:00
parent 8caf85917b
commit 5456939d81
6 changed files with 106 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

48
exps/NATS-Bench/Analyze-time.py Normal file
View File

@ -0,0 +1,48 @@
##############################################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
##############################################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
##############################################################################
# python ./exps/NATS-Bench/Analyze-time.py #
##############################################################################
import os, sys, time, tqdm, torch, random, argparse
from typing import List, Text, Dict, Any
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from copy import deepcopy
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import dict2config, load_config
from datasets import get_datasets
from nats_bench import create
def show_time(api):
print('Show the time for {:} with 12-epoch-training'.format(api))
all_cifar10_time, all_cifar100_time, all_imagenet_time = 0, 0, 0
for index in tqdm.tqdm(range(len(api))):
info = api.get_more_info(index, 'ImageNet16-120', hp='12')
imagenet_time = info['train-all-time']
info = api.get_more_info(index, 'cifar10-valid', hp='12')
cifar10_time = info['train-all-time']
info = api.get_more_info(index, 'cifar100', hp='12')
cifar100_time = info['train-all-time']
# accumulate the time
all_cifar10_time += cifar10_time
all_cifar100_time += cifar100_time
all_imagenet_time += imagenet_time
print('The total training time for CIFAR-10 (held-out train set) is {:} seconds'.format(all_cifar10_time))
print('The total training time for CIFAR-100 (held-out train set) is {:} seconds, {:.2f} times longer than that on CIFAR-10'.format(all_cifar100_time, all_cifar100_time / all_cifar10_time))
print('The total training time for ImageNet-16-120 (held-out train set) is {:} seconds, {:.2f} times longer than that on CIFAR-10'.format(all_imagenet_time, all_imagenet_time / all_cifar10_time))
if __name__ == '__main__':
api_nats_tss = create(None, 'tss', fast_mode=True, verbose=False)
show_time(api_nats_tss)
api_nats_sss = create(None, 'sss', fast_mode=True, verbose=False)
show_time(api_nats_sss)

3
exps/NATS-algos/bohb.py
View File

@ -169,7 +169,8 @@ if __name__ == '__main__':
api = create(None, args.search_space, fast_mode=True, verbose=False) api = create(None, args.search_space, fast_mode=True, verbose=False)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'BOHB') args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
'{:}-T{:}'.format(args.dataset, args.time_budget), 'BOHB')
print('save-dir : {:}'.format(args.save_dir)) print('save-dir : {:}'.format(args.save_dir))
if args.rand_seed < 0: if args.rand_seed < 0:

3
exps/NATS-algos/random_wo_share.py
View File

@ -73,7 +73,8 @@ if __name__ == '__main__':
api = create(None, args.search_space, fast_mode=True, verbose=False) api = create(None, args.search_space, fast_mode=True, verbose=False)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'RANDOM') args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
'{:}-T{:}'.format(args.dataset, args.time_budget), 'RANDOM')
print('save-dir : {:}'.format(args.save_dir)) print('save-dir : {:}'.format(args.save_dir))
if args.rand_seed < 0: if args.rand_seed < 0:

3
exps/NATS-algos/regularized_ea.py
View File

@ -200,7 +200,8 @@ if __name__ == '__main__':
api = create(None, args.search_space, fast_mode=True, verbose=False) api = create(None, args.search_space, fast_mode=True, verbose=False)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'R-EA-SS{:}'.format(args.ea_sample_size)) args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
'{:}-T{:}'.format(args.dataset, args.time_budget), 'R-EA-SS{:}'.format(args.ea_sample_size))
print('save-dir : {:}'.format(args.save_dir)) print('save-dir : {:}'.format(args.save_dir))
print('xargs : {:}'.format(args)) print('xargs : {:}'.format(args))

3
exps/NATS-algos/reinforce.py
View File

@ -194,7 +194,8 @@ if __name__ == '__main__':
api = create(None, args.search_space, fast_mode=True, verbose=False) api = create(None, args.search_space, fast_mode=True, verbose=False)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'REINFORCE-{:}'.format(args.learning_rate)) args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
'{:}-T{:}'.format(args.dataset, args.time_budget), 'REINFORCE-{:}'.format(args.learning_rate))
print('save-dir : {:}'.format(args.save_dir)) print('save-dir : {:}'.format(args.save_dir))
if args.rand_seed < 0: if args.rand_seed < 0:

65
exps/NATS-algos/run-all.sh
View File

@ -10,26 +10,61 @@ if [ "$#" -ne 1 ] ;then
exit 1 exit 1
fi fi
datasets="cifar10 cifar100 ImageNet16-120"
alg_type=$1 alg_type=$1
if [ "$alg_type" == "mul" ]; then if [ "$alg_type" == "mul" ]; then
search_spaces="tss sss" # datasets="cifar10 cifar100 ImageNet16-120"
# The topology search space
dataset="cifar10"
search_space="tss"
time_budget="20000"
python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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
python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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
for dataset in ${datasets} dataset="cifar100"
do search_space="tss"
for search_space in ${search_spaces} time_budget="40000"
do python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.01 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
python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3 python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} 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
python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
done
done
python exps/experimental/vis-bench-algos.py --search_space tss dataset="ImageNet16-120"
python exps/experimental/vis-bench-algos.py --search_space sss search_space="tss"
time_budget="120000"
python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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
python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
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
# The size search space
dataset="cifar10"
search_space="sss"
time_budget="20000"
python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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
python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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
dataset="cifar100"
search_space="sss"
time_budget="40000"
python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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
python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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
dataset="ImageNet16-120"
search_space="tss"
time_budget="60000"
python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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
python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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
# python exps/experimental/vis-bench-algos.py --search_space tss
# python exps/experimental/vis-bench-algos.py --search_space sss
else else
seeds="777 888 999" seeds="777 888 999"
algos="darts-v1 darts-v2 gdas setn random enas" algos="darts-v1 darts-v2 gdas setn random enas"