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This commit is contained in:
D-X-Y
2020-07-30 13:07:11 +00:00
parent df45e68366
commit 6061d74631
21 changed files with 1336 additions and 126 deletions
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15
exps/NAS-Bench-201/test-nas-api.py
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@@ -1,9 +1,11 @@
###############################################################
# NAS-Bench-201, ICLR 2020 (https://arxiv.org/abs/2001.00326) #
###############################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size
###############################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
###############################################################
# Usage: python exps/NAS-Bench-201/test-nas-api.py
# Usage: python exps/NAS-Bench-201/test-nas-api.py #
###############################################################
import os, sys, time, torch, argparse
import numpy as np
@@ -21,7 +23,7 @@ import matplotlib.ticker as ticker
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 nas_201_api import NASBench201API, NASBench301API
from nats_bench import create
from log_utils import time_string
from models import get_cell_based_tiny_net, CellStructure
@@ -97,15 +99,14 @@ def test_issue_81_82(api):
if __name__ == '__main__':
api201 = NASBench201API(os.path.join(os.environ['TORCH_HOME'], 'NAS-Bench-201-v1_0-e61699.pth'), verbose=True)
api201 = create(os.path.join(os.environ['TORCH_HOME'], 'NAS-Bench-201-v1_0-e61699.pth'), 'topology', True)
test_issue_81_82(api201)
# test_api(api201, False)
print ('Test {:} done'.format(api201))
api201 = NASBench201API(None, verbose=True)
api201 = create(None, 'topology', True) # use the default file path
test_issue_81_82(api201)
test_api(api201, False)
print ('Test {:} done'.format(api201))
# api301 = NASBench301API(None, verbose=True)
# test_api(api301, True)
api301 = create(None, 'size', True)
test_api(api301, True)

2
exps/NAS-Bench-201/xshape-collect.py
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@@ -16,7 +16,7 @@ from log_utils import AverageMeter, time_string, convert_secs2time
from config_utils import dict2config
# NAS-Bench-201 related module or function
from models import CellStructure, get_cell_based_tiny_net
from nas_201_api import NASBench301API, ArchResults, ResultsCount
from nas_201_api import ArchResults, ResultsCount
from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders

4
exps/algos-v2/README.md
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@@ -1 +1,3 @@
# Benchmarking NAS Algorithms
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size
# Benchmarking 13 NAS Algorithm

9
exps/algos-v2/bohb.py
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@@ -18,7 +18,7 @@ from config_utils import load_config
from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_201_api import NASBench201API, NASBench301API
from nats_bench import create
from models import CellStructure, get_search_spaces
# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
import ConfigSpace
@@ -167,12 +167,7 @@ if __name__ == '__main__':
parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':
api = NASBench201API(verbose=False)
elif args.search_space == 'sss':
api = NASBench301API(verbose=False)
else:
raise ValueError('Invalid search space : {:}'.format(args.search_space))
api = create(None, args.search_space, verbose=False)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'BOHB')
print('save-dir : {:}'.format(args.save_dir))

9
exps/algos-v2/random_wo_share.py
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@@ -21,7 +21,7 @@ from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_che
from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_search_spaces
from nas_201_api import NASBench201API, NASBench301API
from nats_bench import create
from regularized_ea import random_topology_func, random_size_func
@@ -71,12 +71,7 @@ if __name__ == '__main__':
parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':
api = NASBench201API(verbose=False)
elif args.search_space == 'sss':
api = NASBench301API(verbose=False)
else:
raise ValueError('Invalid search space : {:}'.format(args.search_space))
api = create(None, args.search_space, verbose=False)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'RANDOM')
print('save-dir : {:}'.format(args.save_dir))

50
exps/algos-v2/regularized_ea.py
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@@ -23,8 +23,8 @@ from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_201_api import NASBench201API, NASBench301API
from models import CellStructure, get_search_spaces
from nats_bench import create
class Model(object):
@@ -38,47 +38,6 @@ class Model(object):
return '{:}'.format(self.arch)
# This function is to mimic the training and evaluatinig procedure for a single architecture `arch`.
# The time_cost is calculated as the total training time for a few (e.g., 12 epochs) plus the evaluation time for one epoch.
# For use_012_epoch_training = True, the architecture is trained for 12 epochs, with LR being decaded from 0.1 to 0.
# In this case, the LR schedular is converged.
# For use_012_epoch_training = False, the architecture is planed to be trained for 200 epochs, but we early stop its procedure.
#
def train_and_eval(arch, nas_bench, extra_info, dataname='cifar10-valid', use_012_epoch_training=True):
if use_012_epoch_training and nas_bench is not None:
arch_index = nas_bench.query_index_by_arch( arch )
assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
#_, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs
elif not use_012_epoch_training and nas_bench is not None:
# Please contact me if you want to use the following logic, because it has some potential issues.
# Please use `use_012_epoch_training=False` for cifar10 only.
# It did return values for cifar100 and ImageNet16-120, but it has some potential issues. (Please email me for more details)
arch_index, nepoch = nas_bench.query_index_by_arch( arch ), 25
assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
xoinfo = nas_bench.get_more_info(arch_index, 'cifar10-valid', iepoch=None, hp='12')
xocost = nas_bench.get_cost_info(arch_index, 'cifar10-valid', hp='200')
info = nas_bench.get_more_info(arch_index, dataname, nepoch, hp='200', is_random=True) # use the validation accuracy after 25 training epochs, which is used in our ICLR submission (not the camera ready).
cost = nas_bench.get_cost_info(arch_index, dataname, hp='200')
# The following codes are used to estimate the time cost.
# When we build NAS-Bench-201, architectures are trained on different machines and we can not use that time record.
# When we create checkpoints for converged_LR, we run all experiments on 1080Ti, and thus the time for each architecture can be fairly compared.
nums = {'ImageNet16-120-train': 151700, 'ImageNet16-120-valid': 3000,
'cifar10-valid-train' : 25000, 'cifar10-valid-valid' : 25000,
'cifar100-train' : 50000, 'cifar100-valid' : 5000}
estimated_train_cost = xoinfo['train-per-time'] / nums['cifar10-valid-train'] * nums['{:}-train'.format(dataname)] / xocost['latency'] * cost['latency'] * nepoch
estimated_valid_cost = xoinfo['valid-per-time'] / nums['cifar10-valid-valid'] * nums['{:}-valid'.format(dataname)] / xocost['latency'] * cost['latency']
try:
valid_acc, time_cost = info['valid-accuracy'], estimated_train_cost + estimated_valid_cost
except:
valid_acc, time_cost = info['valtest-accuracy'], estimated_train_cost + estimated_valid_cost
else:
# train a model from scratch.
raise ValueError('NOT IMPLEMENT YET')
return valid_acc, time_cost
def random_topology_func(op_names, max_nodes=4):
# Return a random architecture
def random_architecture():
@@ -239,12 +198,7 @@ if __name__ == '__main__':
parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':
api = NASBench201API(verbose=False)
elif args.search_space == 'sss':
api = NASBench301API(verbose=False)
else:
raise ValueError('Invalid search space : {:}'.format(args.search_space))
api = create(None, args.search_space, 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))
print('save-dir : {:}'.format(args.save_dir))

9
exps/algos-v2/reinforce.py
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@@ -24,8 +24,8 @@ from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_201_api import NASBench201API, NASBench301API
from models import CellStructure, get_search_spaces
from nats_bench import create
class PolicyTopology(nn.Module):
@@ -192,12 +192,7 @@ if __name__ == '__main__':
parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':
api = NASBench201API(verbose=False)
elif args.search_space == 'sss':
api = NASBench301API(verbose=False)
else:
raise ValueError('Invalid search space : {:}'.format(args.search_space))
api = create(None, args.search_space, verbose=False)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'REINFORCE-{:}'.format(args.learning_rate))
print('save-dir : {:}'.format(args.save_dir))

4
exps/algos-v2/search-cell.py
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@@ -39,7 +39,7 @@ from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_che
from utils import count_parameters_in_MB, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net, get_search_spaces
from nas_201_api import NASBench201API as API
from nats_bench import create
# The following three functions are used for DARTS-V2
@@ -364,7 +364,7 @@ def main(xargs):
logger.log('The parameters of the search model = {:.2f} MB'.format(params))
logger.log('search-space : {:}'.format(search_space))
if bool(xargs.use_api):
api = API(verbose=False)
api = create(None, 'topology', verbose=False)
else:
api = None
logger.log('{:} create API = {:} done'.format(time_string(), api))

6
exps/algos-v2/search-size.py
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@@ -27,7 +27,7 @@ from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_che
from utils import count_parameters_in_MB, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net, get_search_spaces
from nas_201_api import NASBench301API as API
from nats_bench import create
# Ad-hoc for TuNAS
@@ -176,7 +176,7 @@ def main(xargs):
logger.log('The parameters of the search model = {:.2f} MB'.format(params))
logger.log('search-space : {:}'.format(search_space))
if bool(xargs.use_api):
api = API(verbose=False)
api = create(None, 'size', verbose=False)
else:
api = None
logger.log('{:} create API = {:} done'.format(time_string(), api))
@@ -291,7 +291,7 @@ if __name__ == '__main__':
parser.add_argument('--rand_seed', type=int, help='manual seed')
args = parser.parse_args()
if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
dirname = '{:}-affine{:}_BN{:}'.format(args.algo, args.affine, args.track_running_stats)
dirname = '{:}-affine{:}_BN{:}-AWD{:}'.format(args.algo, args.affine, args.track_running_stats, args.arch_weight_decay)
if args.overwite_epochs is not None:
dirname = dirname + '-E{:}'.format(args.overwite_epochs)
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, dirname)

2
exps/experimental/example-nas-bench.py
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@@ -16,7 +16,7 @@ matplotlib.use('agg')
import matplotlib.pyplot as plt
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from nas_201_api import NASBench201API, NASBench301API
from nas_201_api import NASBench201API
from log_utils import time_string
from models import get_cell_based_tiny_net
from utils import weight_watcher

14
exps/experimental/test-ww-bench.py
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@@ -3,9 +3,6 @@
###########################################################################################################################################################
# Before run these commands, the files must be properly put.
#
# python exps/experimental/test-ww-bench.py --base_path $HOME/.torch/NAS-Bench-201-v1_0-e61699
# python exps/experimental/test-ww-bench.py --base_path $HOME/.torch/NAS-Bench-201-v1_1-096897 --dataset cifar10-valid --use_12 1 --use_valid 1
# CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/experimental/test-ww-bench.py --base_path $HOME/.torch/NAS-Bench-201-v1_1-096897 --dataset cifar10
# CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/experimental/test-ww-bench.py --search_space sss --base_path $HOME/.torch/NAS-Bench-301-v1_0 --dataset cifar10
# CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/experimental/test-ww-bench.py --search_space sss --base_path $HOME/.torch/NAS-Bench-301-v1_0 --dataset cifar100
# CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=4 python exps/experimental/test-ww-bench.py --search_space sss --base_path $HOME/.torch/NAS-Bench-301-v1_0 --dataset ImageNet16-120
@@ -22,8 +19,8 @@ matplotlib.use('agg')
import matplotlib.pyplot as plt
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from nas_201_api import NASBench201API, NASBench301API
from log_utils import time_string
from nats_bench import create
from models import get_cell_based_tiny_net
from utils import weight_watcher
@@ -52,8 +49,8 @@ def evaluate(api, weight_dir, data: str):
# compute the weight watcher results
config = api.get_net_config(arch_index, data)
net = get_cell_based_tiny_net(config)
meta_info = api.query_meta_info_by_index(arch_index, hp='200' if isinstance(api, NASBench201API) else '90')
params = meta_info.get_net_param(data, 888 if isinstance(api, NASBench201API) else 777)
meta_info = api.query_meta_info_by_index(arch_index, hp='200' if api.search_space_name == 'topology' else '90')
params = meta_info.get_net_param(data, 888 if api.search_space_name == 'topology' else 777)
with torch.no_grad():
net.load_state_dict(params)
_, summary = weight_watcher.analyze(net, alphas=False)
@@ -70,7 +67,7 @@ def evaluate(api, weight_dir, data: str):
ok += 1
norms.append(cur_norm)
# query the accuracy
info = meta_info.get_metrics(data, 'ori-test', iepoch=None, is_random=888 if isinstance(api, NASBench201API) else 777)
info = meta_info.get_metrics(data, 'ori-test', iepoch=None, is_random=888 if api.search_space_name == 'topology' else 777)
accuracies.append(info['accuracy'])
del net, meta_info
# print the information
@@ -81,9 +78,8 @@ def evaluate(api, weight_dir, data: str):
def main(search_space, meta_file: str, weight_dir, save_dir, xdata):
API = NASBench201API if search_space == 'tss' else NASBench301API
save_dir.mkdir(parents=True, exist_ok=True)
api = API(meta_file, verbose=False)
api = create(meta_file, search_space, verbose=False)
datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
print(time_string() + ' ' + '='*50)
for data in datasets:

28
exps/experimental/vis-bench-algos.py → exps/experimental/vis-nats-bench-algos.py
View File

@@ -3,8 +3,8 @@
###############################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
###############################################################
# Usage: python exps/experimental/vis-bench-algos.py --search_space tss
# Usage: python exps/experimental/vis-bench-algos.py --search_space sss
# Usage: python exps/experimental/vis-nats-bench-algos.py --search_space tss
# Usage: python exps/experimental/vis-nats-bench-algos.py --search_space sss
###############################################################
import os, gc, sys, time, torch, argparse
import numpy as np
@@ -22,7 +22,7 @@ import matplotlib.ticker as ticker
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 nas_201_api import NASBench201API, NASBench301API
from nats_bench import create
from log_utils import time_string
@@ -48,18 +48,19 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
def query_performance(api, data, dataset, ticket):
results, is_301 = [], isinstance(api, NASBench301API)
results, is_size_space = [], api.search_space_name == 'size'
for i, info in data.items():
time_w_arch = sorted(info['time_w_arch'], key=lambda x: abs(x[0]-ticket))
time_a, arch_a = time_w_arch[0]
time_b, arch_b = time_w_arch[1]
info_a = api.get_more_info(arch_a, dataset=dataset, hp=90 if is_301 else 200, is_random=False)
info_b = api.get_more_info(arch_b, dataset=dataset, hp=90 if is_301 else 200, is_random=False)
info_a = api.get_more_info(arch_a, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
info_b = api.get_more_info(arch_b, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
accuracy_a, accuracy_b = info_a['test-accuracy'], info_b['test-accuracy']
interplate = (time_b-ticket) / (time_b-time_a) * accuracy_a + (ticket-time_a) / (time_b-time_a) * accuracy_b
results.append(interplate)
return sum(results) / len(results)
y_min_s = {('cifar10', 'tss'): 90,
('cifar10', 'sss'): 92,
('cifar100', 'tss'): 65,
@@ -74,6 +75,10 @@ y_max_s = {('cifar10', 'tss'): 94.5,
('ImageNet16-120', 'tss'): 44,
('ImageNet16-120', 'sss'): 46}
name2label = {'cifar10': 'CIFAR-10',
'cifar100': 'CIFAR-100',
'ImageNet16-120': 'ImageNet-16-120'}
def visualize_curve(api, vis_save_dir, search_space, max_time):
vis_save_dir = vis_save_dir.resolve()
vis_save_dir.mkdir(parents=True, exist_ok=True)
@@ -99,8 +104,8 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
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(dataset), fontsize=LabelSize)
ax.set_title('Searching results on {:}'.format(dataset), fontsize=LabelSize+4)
ax.set_ylabel('Test accuracy on {:}'.format(name2label[dataset]), fontsize=LabelSize)
ax.set_title('Searching results on {:}'.format(name2label[dataset]), fontsize=LabelSize+4)
ax.legend(loc=4, fontsize=LegendFontsize)
fig, axs = plt.subplots(1, 3, figsize=figsize)
@@ -123,10 +128,5 @@ if __name__ == '__main__':
save_dir = Path(args.save_dir)
if args.search_space == 'tss':
api = NASBench201API(verbose=False)
elif args.search_space == 'sss':
api = NASBench301API(verbose=False)
else:
raise ValueError('Invalid search space : {:}'.format(args.search_space))
api = create(None, args.search_space, verbose=False)
visualize_curve(api, save_dir, args.search_space, args.max_time)

30
exps/experimental/vis-bench-ws.py → exps/experimental/vis-nats-bench-ws.py
View File

@@ -3,8 +3,8 @@
###############################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
###############################################################
# Usage: python exps/experimental/vis-bench-ws.py --search_space tss
# Usage: python exps/experimental/vis-bench-ws.py --search_space sss
# Usage: python exps/experimental/vis-nats-bench-ws.py --search_space tss
# Usage: python exps/experimental/vis-nats-bench-ws.py --search_space sss
###############################################################
import os, gc, sys, time, torch, argparse
import numpy as np
@@ -22,15 +22,16 @@ import matplotlib.ticker as ticker
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 nas_201_api import NASBench201API, NASBench301API
from nats_bench import create
from log_utils import time_string
def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
ss_dir = '{:}-{:}'.format(root_dir, search_space)
alg2name, alg2path = OrderedDict(), OrderedDict()
seeds = [777, 888, 999]
print('\n[fetch data] from {:} on {:}'.format(search_space, dataset))
if search_space == 'tss':
seeds = [777]
alg2name['GDAS'] = 'gdas-affine0_BN0-None'
alg2name['RSPS'] = 'random-affine0_BN0-None'
alg2name['DARTS (1st)'] = 'darts-v1-affine0_BN0-None'
@@ -38,7 +39,6 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
alg2name['ENAS'] = 'enas-affine0_BN0-None'
alg2name['SETN'] = 'setn-affine0_BN0-None'
else:
seeds = [777, 888, 999]
alg2name['TAS'] = 'tas-affine0_BN0'
alg2name['FBNetV2'] = 'fbv2-affine0_BN0'
alg2name['TuNAS'] = 'tunas-affine0_BN0'
@@ -46,13 +46,19 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
alg2data = OrderedDict()
for alg, path in alg2path.items():
alg2data[alg] = []
alg2data[alg], ok_num = [], 0
for seed in seeds:
xpath = path.format(seed)
assert os.path.isfile(xpath), 'invalid path : {:}'.format(xpath)
if os.path.isfile(xpath):
ok_num += 1
else:
print('This is an invalid path : {:}'.format(xpath))
continue
data = torch.load(xpath, map_location=torch.device('cpu'))
data = torch.load(data['last_checkpoint'], map_location=torch.device('cpu'))
alg2data[alg].append(data['genotypes'])
print('This algorithm : {:} has {:} valid ckps.'.format(alg, ok_num))
assert ok_num > 0, 'Must have at least 1 valid ckps.'
return alg2data
@@ -95,7 +101,7 @@ def visualize_curve(api, vis_save_dir, search_space):
for iepoch in range(epochs+1):
structures, accs = [_[iepoch-1] for _ in data], []
for structure in structures:
info = api.get_more_info(structure, dataset=dataset, hp=90 if isinstance(api, NASBench301API) else 200, is_random=False)
info = api.get_more_info(structure, dataset=dataset, hp=90 if api.search_space_name == 'size' else 200, is_random=False)
accs.append(info['test-accuracy'])
accuracies.append(sum(accs)/len(accs))
xs.append(iepoch)
@@ -124,12 +130,6 @@ if __name__ == '__main__':
args = parser.parse_args()
save_dir = Path(args.save_dir)
alg2data = fetch_data(search_space='tss', dataset='cifar10')
if args.search_space == 'tss':
api = NASBench201API(verbose=False)
elif args.search_space == 'sss':
api = NASBench301API(verbose=False)
else:
raise ValueError('Invalid search space : {:}'.format(args.search_space))
api = create(None, args.search_space, verbose=False)
visualize_curve(api, save_dir, args.search_space)

6
exps/experimental/visualize-nas-bench-x.py
View File

@@ -21,9 +21,9 @@ import matplotlib.ticker as ticker
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 nas_201_api import NASBench201API, NASBench301API
from log_utils import time_string
from models import get_cell_based_tiny_net
from nats_bench import create
def visualize_info(api, vis_save_dir, indicator):
@@ -391,11 +391,11 @@ if __name__ == '__main__':
to_save_dir = Path(args.save_dir)
datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
api201 = NASBench201API(None, verbose=True)
api201 = create(None, 'tss', verbose=True)
for xdata in datasets:
visualize_tss_info(api201, xdata, to_save_dir)
api301 = NASBench301API(None, verbose=True)
api301 = create(None, 'size', verbose=True)
for xdata in datasets:
visualize_sss_info(api301, xdata, to_save_dir)