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Update VIS-CODES and SCRIPTS

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D-X-Y 2020-07-22 12:48:40 +00:00
parent 8d27050f6f
commit a2a1abcb7d
3 changed files with 55 additions and 21 deletions
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48
exps/algos-v2/run-all.sh
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@ -1,22 +1,46 @@
#!/bin/bash #!/bin/bash
# bash ./exps/algos-v2/run-all.sh # bash ./exps/algos-v2/run-all.sh mul
# bash ./exps/algos-v2/run-all.sh ws
set -e set -e
echo script name: $0 echo script name: $0
echo $# arguments echo $# arguments
if [ "$#" -ne 1 ] ;then
echo "Input illegal number of parameters " $#
echo "Need 1 parameters for type of algorithms."
exit 1
fi
datasets="cifar10 cifar100 ImageNet16-120" datasets="cifar10 cifar100 ImageNet16-120"
search_spaces="tss sss" alg_type=$1
for dataset in ${datasets} if [ "$alg_type" == "mul" ]; then
do search_spaces="tss sss"
for search_space in ${search_spaces}
for dataset in ${datasets}
do do
python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.01 for search_space in ${search_spaces}
python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3 do
python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space} python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.01
python ./exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
python ./exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
done
done done
done
python exps/experimental/vis-bench-algos.py --search_space tss python exps/experimental/vis-bench-algos.py --search_space tss
python exps/experimental/vis-bench-algos.py --search_space sss python exps/experimental/vis-bench-algos.py --search_space sss
else
seeds="777 888 999"
epoch=200
for seed in ${seeds}
do
for alg in "darts-v1 darts-v2 gdas setn random enas"
do
python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo ${alg} --rand_seed ${seed} --overwite_epochs ${epoch}
python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo ${alg} --rand_seed ${seed} --overwite_epochs ${epoch}
python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo ${alg} --rand_seed ${seed} --overwite_epochs ${epoch}
done
done
fi

22
exps/algos-v2/search-cell.py
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@ -22,8 +22,8 @@
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo random # python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo random
#### ####
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777 # python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo enas # python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo enas # python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
###################################################################################### ######################################################################################
import os, sys, time, random, argparse import os, sys, time, random, argparse
import numpy as np import numpy as np
@ -333,7 +333,11 @@ def main(xargs):
logger = prepare_logger(args) logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1) train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger) if xargs.overwite_epochs is None:
extra_info = {'class_num': class_num, 'xshape': xshape}
else:
extra_info = {'class_num': class_num, 'xshape': xshape, 'epochs': xargs.overwite_epochs}
config = load_config(xargs.config_path, extra_info, logger)
search_loader, train_loader, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \ search_loader, train_loader, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
(config.batch_size, config.test_batch_size), xargs.workers) (config.batch_size, config.test_batch_size), xargs.workers)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size)) logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
@ -496,6 +500,7 @@ if __name__ == '__main__':
parser.add_argument('--track_running_stats',type=int, default=0, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.') parser.add_argument('--track_running_stats',type=int, default=0, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
parser.add_argument('--affine' , type=int, default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.') parser.add_argument('--affine' , type=int, default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.')
parser.add_argument('--config_path' , type=str, default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.') parser.add_argument('--config_path' , type=str, default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.')
parser.add_argument('--overwite_epochs', type=int, help='The number of epochs to overwrite that value in config files.')
# architecture leraning rate # architecture leraning rate
parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding') parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay' , type=float, default=1e-3, help='weight decay for arch encoding') parser.add_argument('--arch_weight_decay' , type=float, default=1e-3, help='weight decay for arch encoding')
@ -508,8 +513,13 @@ if __name__ == '__main__':
parser.add_argument('--rand_seed', type=int, help='manual seed') parser.add_argument('--rand_seed', type=int, help='manual seed')
args = parser.parse_args() args = parser.parse_args()
if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000) if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), if args.overwite_epochs is None:
args.dataset, args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
'{:}-affine{:}_BN{:}-{:}'.format(args.algo, args.affine, args.track_running_stats, args.drop_path_rate)) args.dataset,
'{:}-affine{:}_BN{:}-{:}'.format(args.algo, args.affine, args.track_running_stats, args.drop_path_rate))
else:
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
args.dataset,
'{:}-affine{:}_BN{:}-E{:}-{:}'.format(args.algo, args.affine, args.track_running_stats, args.overwite_epochs, args.drop_path_rate))
main(args) main(args)

6
exps/experimental/vis-bench-ws.py
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@ -30,12 +30,12 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
ss_dir = '{:}-{:}'.format(root_dir, search_space) ss_dir = '{:}-{:}'.format(root_dir, search_space)
alg2name, alg2path = OrderedDict(), OrderedDict() alg2name, alg2path = OrderedDict(), OrderedDict()
seeds = [777] seeds = [777]
alg2name['GDAS'] = 'gdas-affine1_BN0-None' alg2name['GDAS'] = 'gdas-affine0_BN0-None'
alg2name['RSPS'] = 'random-affine0_BN0-None'
""" """
alg2name['DARTS (1st)'] = 'darts-v1-affine1_BN0-None' alg2name['DARTS (1st)'] = 'darts-v1-affine1_BN0-None'
alg2name['DARTS (2nd)'] = 'darts-v2-affine1_BN0-None' alg2name['DARTS (2nd)'] = 'darts-v2-affine1_BN0-None'
alg2name['SETN'] = 'setn-affine1_BN0-None' alg2name['SETN'] = 'setn-affine1_BN0-None'
alg2name['RSPS'] = 'random-affine1_BN0-None'
""" """
for alg, name in alg2name.items(): for alg, name in alg2name.items():
alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth') alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
@ -76,7 +76,7 @@ def visualize_curve(api, vis_save_dir, search_space):
def sub_plot_fn(ax, dataset): def sub_plot_fn(ax, dataset):
alg2data = fetch_data(search_space=search_space, dataset=dataset) alg2data = fetch_data(search_space=search_space, dataset=dataset)
alg2accuracies = OrderedDict() alg2accuracies = OrderedDict()
epochs = 20 epochs = 100
colors = ['b', 'g', 'c', 'm', 'y'] colors = ['b', 'g', 'c', 'm', 'y']
ax.set_xlim(0, epochs) ax.set_xlim(0, epochs)
# ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)]) # ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])