iicd/autodl-projects
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update plots for NATS-Bench

Browse Source
This commit is contained in:
D-X-Y
2020-11-27 02:15:09 +08:00
parent 3fb3222e82
commit 550d24ec07
3 changed files with 43 additions and 60 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
exps/NATS-Bench/draw-fig6.py
View File

@@ -33,7 +33,7 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
alg2name['REA'] = 'R-EA-SS3'
alg2name['REINFORCE'] = 'REINFORCE-0.01'
alg2name['RANDOM'] = 'RANDOM'
alg2name['BOHB'] = 'BOHB'
# alg2name['BOHB'] = 'BOHB'
for alg, name in alg2name.items():
alg2path[alg] = os.path.join(ss_dir, dataset, name, 'results.pth')
assert os.path.isfile(alg2path[alg]), 'invalid path : {:}'.format(alg2path[alg])
@@ -76,11 +76,18 @@ y_max_s = {('cifar10', 'tss'): 94.5,
('ImageNet16-120', 'tss'): 44,
('ImageNet16-120', 'sss'): 46}
x_axis_s = {('cifar10', 'tss'): 200,
('cifar10', 'sss'): 200,
('cifar100', 'tss'): 400,
('cifar100', 'sss'): 400,
('ImageNet16-120', 'tss'): 1200,
('ImageNet16-120', 'sss'): 600}
name2label = {'cifar10': 'CIFAR-10',
'cifar100': 'CIFAR-100',
'ImageNet16-120': 'ImageNet-16-120'}
def visualize_curve(api, vis_save_dir, search_space, max_time):
def visualize_curve(api, vis_save_dir, search_space):
vis_save_dir = vis_save_dir.resolve()
vis_save_dir.mkdir(parents=True, exist_ok=True)
@@ -89,28 +96,36 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
LabelSize, LegendFontsize = 16, 16
def sub_plot_fn(ax, dataset):
xdataset, max_time = dataset.split('-T')
alg2data = fetch_data(search_space=search_space, dataset=dataset)
alg2accuracies = OrderedDict()
total_tickets = 150
time_tickets = [float(i) / total_tickets * max_time for i in range(total_tickets)]
time_tickets = [float(i) / total_tickets * int(max_time) for i in range(total_tickets)]
colors = ['b', 'g', 'c', 'm', 'y']
ax.set_xlim(0, 200)
ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
ax.set_xlim(0, x_axis_s[(xdataset, search_space)])
ax.set_ylim(y_min_s[(xdataset, search_space)],
y_max_s[(xdataset, search_space)])
for idx, (alg, data) in enumerate(alg2data.items()):
print('plot alg : {:}'.format(alg))
print('{:} plot alg : {:}'.format(time_string(), alg))
accuracies = []
for ticket in time_tickets:
accuracy = query_performance(api, data, dataset, ticket)
accuracy = query_performance(api, data, xdataset, ticket)
accuracies.append(accuracy)
alg2accuracies[alg] = accuracies
ax.plot([x/100 for x in time_tickets], accuracies, c=colors[idx], label='{:}'.format(alg))
ax.set_xlabel('Estimated wall-clock time (1e2 seconds)', fontsize=LabelSize)
ax.set_ylabel('Test accuracy on {:}'.format(name2label[dataset]), fontsize=LabelSize)
ax.set_title('Searching results on {:}'.format(name2label[dataset]), fontsize=LabelSize+4)
ax.set_ylabel('Test accuracy on {:}'.format(name2label[xdataset]), fontsize=LabelSize)
ax.set_title('Searching results on {:}'.format(name2label[xdataset]), fontsize=LabelSize+4)
ax.legend(loc=4, fontsize=LegendFontsize)
fig, axs = plt.subplots(1, 3, figsize=figsize)
datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
# datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
if search_space == 'tss':
datasets = ['cifar10-T20000', 'cifar100-T40000', 'ImageNet16-120-T120000']
elif search_space == 'sss':
datasets = ['cifar10-T20000', 'cifar100-T40000', 'ImageNet16-120-T60000']
else:
raise ValueError('Unknown search space: {:}'.format(search_space))
for dataset, ax in zip(datasets, axs):
sub_plot_fn(ax, dataset)
print('sub-plot {:} on {:} done.'.format(dataset, search_space))
@@ -121,13 +136,12 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='NATS-Bench', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = argparse.ArgumentParser(description='NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos', help='Folder to save checkpoints and log.')
parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
parser.add_argument('--max_time', type=float, default=20000, help='The maximum time budget.')
args = parser.parse_args()
save_dir = Path(args.save_dir)
api = create(None, args.search_space, fast_mode=True, verbose=False)
visualize_curve(api, save_dir, args.search_space, args.max_time)
visualize_curve(api, save_dir, args.search_space)

2
exps/NATS-algos/bohb.py
View File

@@ -167,7 +167,7 @@ if __name__ == '__main__':
parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
api = create(None, args.search_space, fast_mode=True, verbose=False)
api = create(None, args.search_space, fast_mode=False, verbose=False)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
'{:}-T{:}'.format(args.dataset, args.time_budget), 'BOHB')

61
exps/NATS-algos/run-all.sh
View File

@@ -14,55 +14,24 @@ alg_type=$1
if [ "$alg_type" == "mul" ]; then
# datasets="cifar10 cifar100 ImageNet16-120"
run_four_algorithms(){
dataset=$1
search_space=$2
time_budget=$3
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
}
# 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
dataset="cifar100"
search_space="tss"
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="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
run_four_algorithms "cifar10" "tss" "20000"
run_four_algorithms "cifar100" "tss" "40000"
run_four_algorithms "ImageNet16-120" "tss" "120000"
# 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
run_four_algorithms "cifar10" "sss" "20000"
run_four_algorithms "cifar100" "sss" "40000"
run_four_algorithms "ImageNet16-120" "sss" "60000"
# python exps/experimental/vis-bench-algos.py --search_space tss
# python exps/experimental/vis-bench-algos.py --search_space sss
else