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Create NATS

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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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1
CHANGE-LOG.md
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@ -5,3 +5,4 @@
- [2019.09.28] [f8f3f38] TAS and SETN codes were publicly released.
- [2019.01.31] [13e908f] GDAS codes were publicly released.
- [2020.07.01] [a45808b] Upgrade NAS-API to the 2.0 version.
- [2020.07.30] [ ] Create NATS-BENCH.

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)

2
lib/models/__init__.py
View File

@ -64,7 +64,7 @@ def get_search_spaces(xtype, name) -> List[Text]:
assert name in SearchSpaceNames, 'invalid name [{:}] in {:}'.format(name, SearchSpaceNames.keys())
return SearchSpaceNames[name]
elif xtype == 'sss': # The size search space.
if name == 'nas-bench-301':
if name == 'nas-bench-301' or name == 'nats-bench' or name == 'nats-bench-size':
return {'candidates': [8, 16, 24, 32, 40, 48, 56, 64],
'numbers': 5}
else:

1
lib/models/cell_operations.py
View File

@ -25,6 +25,7 @@ NAS_BENCH_201 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3',
DARTS_SPACE = ['none', 'skip_connect', 'dua_sepc_3x3', 'dua_sepc_5x5', 'dil_sepc_3x3', 'dil_sepc_5x5', 'avg_pool_3x3', 'max_pool_3x3']
SearchSpaceNames = {'connect-nas' : CONNECT_NAS_BENCHMARK,
'nats-bench' : NAS_BENCH_201,
'nas-bench-201': NAS_BENCH_201,
'nas-bench-301': NAS_BENCH_201,
'darts' : DARTS_SPACE}

25
lib/nats_bench/__init__.py Normal file
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@ -0,0 +1,25 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
#####################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size
#####################################################
#
#
from .api_utils import ArchResults, ResultsCount
from .api_topology import NATStopology
from .api_size import NATSsize
NATS_BENCH_API_VERSIONs = ['v1.0'] # [2020.07.30]
def version():
return NATS_BENCH_API_VERSIONs[-1]
def create(file_path_or_dict, search_space, verbose=True):
if search_space in ['tss', 'topology']:
return NATStopology(file_path_or_dict, verbose)
elif search_space in ['sss', 'size']:
return NATSsize(file_path_or_dict, verbose)
else:
raise ValueError('invalid search space : {:}'.format(search_space))

222
lib/nats_bench/api_size.py Normal file
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@ -0,0 +1,222 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
############################################################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size
############################################################################################
# The history of benchmark files:
#
import os, copy, random, torch, numpy as np
from pathlib import Path
from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
from .api_utils import ArchResults
from .api_utils import NASBenchMetaAPI
from .api_utils import remap_dataset_set_names
ALL_BENCHMARK_FILES = ['NAS-Bench-301-v1_0-363be7.pth']
ALL_ARCHIVE_DIRS = ['NAS-Bench-301-v1_0-archive']
def print_information(information, extra_info=None, show=False):
dataset_names = information.get_dataset_names()
strings = [information.arch_str, 'datasets : {:}, extra-info : {:}'.format(dataset_names, extra_info)]
def metric2str(loss, acc):
return 'loss = {:.3f} & top1 = {:.2f}%'.format(loss, acc)
for ida, dataset in enumerate(dataset_names):
metric = information.get_compute_costs(dataset)
flop, param, latency = metric['flops'], metric['params'], metric['latency']
str1 = '{:14s} FLOP={:6.2f} M, Params={:.3f} MB, latency={:} ms.'.format(dataset, flop, param, '{:.2f}'.format(latency*1000) if latency is not None and latency > 0 else None)
train_info = information.get_metrics(dataset, 'train')
if dataset == 'cifar10-valid':
valid_info = information.get_metrics(dataset, 'x-valid')
test__info = information.get_metrics(dataset, 'ori-test')
str2 = '{:14s} train : [{:}], valid : [{:}], test : [{:}]'.format(
dataset, metric2str(train_info['loss'], train_info['accuracy']),
metric2str(valid_info['loss'], valid_info['accuracy']),
metric2str(test__info['loss'], test__info['accuracy']))
elif dataset == 'cifar10':
test__info = information.get_metrics(dataset, 'ori-test')
str2 = '{:14s} train : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy']))
else:
valid_info = information.get_metrics(dataset, 'x-valid')
test__info = information.get_metrics(dataset, 'x-test')
str2 = '{:14s} train : [{:}], valid : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy']))
strings += [str1, str2]
if show: print('\n'.join(strings))
return strings
"""
This is the class for the API of size search space in NATS-Bench.
"""
class NATSsize(NASBenchMetaAPI):
""" The initialization function that takes the dataset file path (or a dict loaded from that path) as input. """
def __init__(self, file_path_or_dict: Optional[Union[Text, Dict]]=None, verbose: bool=True):
self.filename = None
self._search_space_name = 'size'
self.reset_time()
if file_path_or_dict is None:
file_path_or_dict = os.path.join(os.environ['TORCH_HOME'], ALL_BENCHMARK_FILES[-1])
print ('Try to use the default NATS-Bench (size) path from {:}.'.format(file_path_or_dict))
if isinstance(file_path_or_dict, str) or isinstance(file_path_or_dict, Path):
file_path_or_dict = str(file_path_or_dict)
if verbose: print('try to create the NATS-Bench (size) api from {:}'.format(file_path_or_dict))
assert os.path.isfile(file_path_or_dict), 'invalid path : {:}'.format(file_path_or_dict)
self.filename = Path(file_path_or_dict).name
file_path_or_dict = torch.load(file_path_or_dict, map_location='cpu')
elif isinstance(file_path_or_dict, dict):
file_path_or_dict = copy.deepcopy( file_path_or_dict )
else: raise ValueError('invalid type : {:} not in [str, dict]'.format(type(file_path_or_dict)))
assert isinstance(file_path_or_dict, dict), 'It should be a dict instead of {:}'.format(type(file_path_or_dict))
self.verbose = verbose # [TODO] a flag indicating whether to print more logs
keys = ('meta_archs', 'arch2infos', 'evaluated_indexes')
for key in keys: assert key in file_path_or_dict, 'Can not find key[{:}] in the dict'.format(key)
self.meta_archs = copy.deepcopy( file_path_or_dict['meta_archs'] )
# This is a dict mapping each architecture to a dict, where the key is #epochs and the value is ArchResults
self.arch2infos_dict = OrderedDict()
self._avaliable_hps = set()
for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
all_infos = file_path_or_dict['arch2infos'][xkey]
hp2archres = OrderedDict()
for hp_key, results in all_infos.items():
hp2archres[hp_key] = ArchResults.create_from_state_dict(results)
self._avaliable_hps.add(hp_key) # save the avaliable hyper-parameter
self.arch2infos_dict[xkey] = hp2archres
self.evaluated_indexes = sorted(list(file_path_or_dict['evaluated_indexes']))
self.archstr2index = {}
for idx, arch in enumerate(self.meta_archs):
assert arch not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch])
self.archstr2index[ arch ] = idx
if self.verbose:
print('Create NATS-Bench (size) done with {:}/{:} architectures avaliable.'.format(len(self.evaluated_indexes), len(self.meta_archs)))
def reload(self, archive_root: Text = None, index: int = None):
"""Overwrite all information of the 'index'-th architecture in the search space, where the data will be loaded from 'archive_root'.
If index is None, overwrite all ckps.
"""
if self.verbose:
print('Call clear_params with archive_root={:} and index={:}'.format(archive_root, index))
if archive_root is None:
archive_root = os.path.join(os.environ['TORCH_HOME'], ALL_ARCHIVE_DIRS[-1])
assert os.path.isdir(archive_root), 'invalid directory : {:}'.format(archive_root)
if index is None:
indexes = list(range(len(self)))
else:
indexes = [index]
for idx in indexes:
assert 0 <= idx < len(self.meta_archs), 'invalid index of {:}'.format(idx)
xfile_path = os.path.join(archive_root, '{:06d}-FULL.pth'.format(idx))
if not os.path.isfile(xfile_path):
xfile_path = os.path.join(archive_root, '{:d}-FULL.pth'.format(idx))
assert os.path.isfile(xfile_path), 'invalid data path : {:}'.format(xfile_path)
xdata = torch.load(xfile_path, map_location='cpu')
assert isinstance(xdata, dict), 'invalid format of data in {:}'.format(xfile_path)
hp2archres = OrderedDict()
for hp_key, results in xdata.items():
hp2archres[hp_key] = ArchResults.create_from_state_dict(results)
self.arch2infos_dict[idx] = hp2archres
def query_info_str_by_arch(self, arch, hp: Text='12'):
""" This function is used to query the information of a specific architecture
'arch' can be an architecture index or an architecture string
When hp=01, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/01E.config'
When hp=12, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/12E.config'
When hp=90, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/90E.config'
The difference between these three configurations are the number of training epochs.
"""
if self.verbose:
print('Call query_info_str_by_arch with arch={:} and hp={:}'.format(arch, hp))
return self._query_info_str_by_arch(arch, hp, print_information)
def get_more_info(self, index, dataset: Text, iepoch=None, hp='12', is_random=True):
"""This function will return the metric for the `index`-th architecture
`dataset` indicates the dataset:
'cifar10-valid' : using the proposed train set of CIFAR-10 as the training set
'cifar10' : using the proposed train+valid set of CIFAR-10 as the training set
'cifar100' : using the proposed train set of CIFAR-100 as the training set
'ImageNet16-120' : using the proposed train set of ImageNet-16-120 as the training set
`iepoch` indicates the index of training epochs from 0 to 11/199.
When iepoch=None, it will return the metric for the last training epoch
When iepoch=11, it will return the metric for the 11-th training epoch (starting from 0)
`hp` indicates different hyper-parameters for training
When hp=01, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 01 epochs
When hp=12, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 12 epochs
When hp=90, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 90 epochs
`is_random`
When is_random=True, the performance of a random architecture will be returned
When is_random=False, the performanceo of all trials will be averaged.
"""
if self.verbose:
print('Call the get_more_info function with index={:}, dataset={:}, iepoch={:}, hp={:}, and is_random={:}.'.format(index, dataset, iepoch, hp, is_random))
index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object
if index not in self.arch2infos_dict:
raise ValueError('Did not find {:} from arch2infos_dict.'.format(index))
archresult = self.arch2infos_dict[index][str(hp)]
# if randomly select one trial, select the seed at first
if isinstance(is_random, bool) and is_random:
seeds = archresult.get_dataset_seeds(dataset)
is_random = random.choice(seeds)
# collect the training information
train_info = archresult.get_metrics(dataset, 'train', iepoch=iepoch, is_random=is_random)
total = train_info['iepoch'] + 1
xinfo = {'train-loss' : train_info['loss'],
'train-accuracy': train_info['accuracy'],
'train-per-time': train_info['all_time'] / total,
'train-all-time': train_info['all_time']}
# collect the evaluation information
if dataset == 'cifar10-valid':
valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
try:
test_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
except:
test_info = None
valtest_info = None
else:
try: # collect results on the proposed test set
if dataset == 'cifar10':
test_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
else:
test_info = archresult.get_metrics(dataset, 'x-test', iepoch=iepoch, is_random=is_random)
except:
test_info = None
try: # collect results on the proposed validation set
valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
except:
valid_info = None
try:
if dataset != 'cifar10':
valtest_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
else:
valtest_info = None
except:
valtest_info = None
if valid_info is not None:
xinfo['valid-loss'] = valid_info['loss']
xinfo['valid-accuracy'] = valid_info['accuracy']
xinfo['valid-per-time'] = valid_info['all_time'] / total
xinfo['valid-all-time'] = valid_info['all_time']
if test_info is not None:
xinfo['test-loss'] = test_info['loss']
xinfo['test-accuracy'] = test_info['accuracy']
xinfo['test-per-time'] = test_info['all_time'] / total
xinfo['test-all-time'] = test_info['all_time']
if valtest_info is not None:
xinfo['valtest-loss'] = valtest_info['loss']
xinfo['valtest-accuracy'] = valtest_info['accuracy']
xinfo['valtest-per-time'] = valtest_info['all_time'] / total
xinfo['valtest-all-time'] = valtest_info['all_time']
return xinfo
def show(self, index: int = -1) -> None:
"""
This function will print the information of a specific (or all) architecture(s).
:param index: If the index < 0: it will loop for all architectures and print their information one by one.
else: it will print the information of the 'index'-th architecture.
:return: nothing
"""
self._show(index, print_information)

269
lib/nats_bench/api_topology.py Normal file
View File

@ -0,0 +1,269 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
############################################################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size
############################################################################################
import os, copy, random, torch, numpy as np
from pathlib import Path
from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
from .api_utils import ArchResults
from .api_utils import NASBenchMetaAPI
from .api_utils import remap_dataset_set_names
ALL_BENCHMARK_FILES = ['NAS-Bench-201-v1_0-e61699.pth', 'NAS-Bench-201-v1_1-096897.pth']
ALL_ARCHIVE_DIRS = ['NAS-Bench-201-v1_1-archive']
def print_information(information, extra_info=None, show=False):
dataset_names = information.get_dataset_names()
strings = [information.arch_str, 'datasets : {:}, extra-info : {:}'.format(dataset_names, extra_info)]
def metric2str(loss, acc):
return 'loss = {:.3f}, top1 = {:.2f}%'.format(loss, acc)
for ida, dataset in enumerate(dataset_names):
metric = information.get_compute_costs(dataset)
flop, param, latency = metric['flops'], metric['params'], metric['latency']
str1 = '{:14s} FLOP={:6.2f} M, Params={:.3f} MB, latency={:} ms.'.format(dataset, flop, param, '{:.2f}'.format(latency*1000) if latency is not None and latency > 0 else None)
train_info = information.get_metrics(dataset, 'train')
if dataset == 'cifar10-valid':
valid_info = information.get_metrics(dataset, 'x-valid')
str2 = '{:14s} train : [{:}], valid : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy']))
elif dataset == 'cifar10':
test__info = information.get_metrics(dataset, 'ori-test')
str2 = '{:14s} train : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy']))
else:
valid_info = information.get_metrics(dataset, 'x-valid')
test__info = information.get_metrics(dataset, 'x-test')
str2 = '{:14s} train : [{:}], valid : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy']))
strings += [str1, str2]
if show: print('\n'.join(strings))
return strings
"""
This is the class for the API of topology search space in NATS-Bench.
"""
class NATStopology(NASBenchMetaAPI):
""" The initialization function that takes the dataset file path (or a dict loaded from that path) as input. """
def __init__(self, file_path_or_dict: Optional[Union[Text, Dict]]=None,
verbose: bool=True):
self.filename = None
self._search_space_name = 'topology'
self.reset_time()
if file_path_or_dict is None:
file_path_or_dict = os.path.join(os.environ['TORCH_HOME'], ALL_BENCHMARK_FILES[-1])
print ('Try to use the default NATS-Bench (topology) path from {:}.'.format(file_path_or_dict))
if isinstance(file_path_or_dict, str) or isinstance(file_path_or_dict, Path):
file_path_or_dict = str(file_path_or_dict)
if verbose: print('try to create the NATS-Bench (topology) api from {:}'.format(file_path_or_dict))
assert os.path.isfile(file_path_or_dict), 'invalid path : {:}'.format(file_path_or_dict)
self.filename = Path(file_path_or_dict).name
file_path_or_dict = torch.load(file_path_or_dict, map_location='cpu')
elif isinstance(file_path_or_dict, dict):
file_path_or_dict = copy.deepcopy(file_path_or_dict)
else: raise ValueError('invalid type : {:} not in [str, dict]'.format(type(file_path_or_dict)))
assert isinstance(file_path_or_dict, dict), 'It should be a dict instead of {:}'.format(type(file_path_or_dict))
self.verbose = verbose # [TODO] a flag indicating whether to print more logs
keys = ('meta_archs', 'arch2infos', 'evaluated_indexes')
for key in keys: assert key in file_path_or_dict, 'Can not find key[{:}] in the dict'.format(key)
self.meta_archs = copy.deepcopy( file_path_or_dict['meta_archs'] )
# This is a dict mapping each architecture to a dict, where the key is #epochs and the value is ArchResults
self.arch2infos_dict = OrderedDict()
self._avaliable_hps = set(['12', '200'])
for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
all_info = file_path_or_dict['arch2infos'][xkey]
hp2archres = OrderedDict()
# self.arch2infos_less[xkey] = ArchResults.create_from_state_dict( all_info['less'] )
# self.arch2infos_full[xkey] = ArchResults.create_from_state_dict( all_info['full'] )
hp2archres['12'] = ArchResults.create_from_state_dict(all_info['less'])
hp2archres['200'] = ArchResults.create_from_state_dict(all_info['full'])
self.arch2infos_dict[xkey] = hp2archres
self.evaluated_indexes = sorted(list(file_path_or_dict['evaluated_indexes']))
self.archstr2index = {}
for idx, arch in enumerate(self.meta_archs):
assert arch not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch])
self.archstr2index[ arch ] = idx
def reload(self, archive_root: Text = None, index: int = None):
"""Overwrite all information of the 'index'-th architecture in the search space.
It will load its data from 'archive_root'.
"""
if archive_root is None:
archive_root = os.path.join(os.environ['TORCH_HOME'], ALL_ARCHIVE_DIRS[-1])
assert os.path.isdir(archive_root), 'invalid directory : {:}'.format(archive_root)
if index is None:
indexes = list(range(len(self)))
else:
indexes = [index]
for idx in indexes:
assert 0 <= idx < len(self.meta_archs), 'invalid index of {:}'.format(idx)
xfile_path = os.path.join(archive_root, '{:06d}-FULL.pth'.format(idx))
assert os.path.isfile(xfile_path), 'invalid data path : {:}'.format(xfile_path)
xdata = torch.load(xfile_path, map_location='cpu')
assert isinstance(xdata, dict) and 'full' in xdata and 'less' in xdata, 'invalid format of data in {:}'.format(xfile_path)
hp2archres = OrderedDict()
hp2archres['12'] = ArchResults.create_from_state_dict(xdata['less'])
hp2archres['200'] = ArchResults.create_from_state_dict(xdata['full'])
self.arch2infos_dict[idx] = hp2archres
def query_info_str_by_arch(self, arch, hp: Text='12'):
""" This function is used to query the information of a specific architecture
'arch' can be an architecture index or an architecture string
When hp=12, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/12E.config'
When hp=200, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/200E.config'
The difference between these three configurations are the number of training epochs.
"""
if self.verbose:
print('Call query_info_str_by_arch with arch={:} and hp={:}'.format(arch, hp))
return self._query_info_str_by_arch(arch, hp, print_information)
# obtain the metric for the `index`-th architecture
# `dataset` indicates the dataset:
# 'cifar10-valid' : using the proposed train set of CIFAR-10 as the training set
# 'cifar10' : using the proposed train+valid set of CIFAR-10 as the training set
# 'cifar100' : using the proposed train set of CIFAR-100 as the training set
# 'ImageNet16-120' : using the proposed train set of ImageNet-16-120 as the training set
# `iepoch` indicates the index of training epochs from 0 to 11/199.
# When iepoch=None, it will return the metric for the last training epoch
# When iepoch=11, it will return the metric for the 11-th training epoch (starting from 0)
# `use_12epochs_result` indicates different hyper-parameters for training
# When use_12epochs_result=True, it trains the network with 12 epochs and the LR decayed from 0.1 to 0 within 12 epochs
# When use_12epochs_result=False, it trains the network with 200 epochs and the LR decayed from 0.1 to 0 within 200 epochs
# `is_random`
# When is_random=True, the performance of a random architecture will be returned
# When is_random=False, the performanceo of all trials will be averaged.
def get_more_info(self, index, dataset, iepoch=None, hp='12', is_random=True):
if self.verbose:
print('Call the get_more_info function with index={:}, dataset={:}, iepoch={:}, hp={:}, and is_random={:}.'.format(index, dataset, iepoch, hp, is_random))
index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object
if index not in self.arch2infos_dict:
raise ValueError('Did not find {:} from arch2infos_dict.'.format(index))
archresult = self.arch2infos_dict[index][str(hp)]
# if randomly select one trial, select the seed at first
if isinstance(is_random, bool) and is_random:
seeds = archresult.get_dataset_seeds(dataset)
is_random = random.choice(seeds)
# collect the training information
train_info = archresult.get_metrics(dataset, 'train', iepoch=iepoch, is_random=is_random)
total = train_info['iepoch'] + 1
xinfo = {'train-loss' : train_info['loss'],
'train-accuracy': train_info['accuracy'],
'train-per-time': train_info['all_time'] / total if train_info['all_time'] is not None else None,
'train-all-time': train_info['all_time']}
# collect the evaluation information
if dataset == 'cifar10-valid':
valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
try:
test_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
except:
test_info = None
valtest_info = None
else:
try: # collect results on the proposed test set
if dataset == 'cifar10':
test_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
else:
test_info = archresult.get_metrics(dataset, 'x-test', iepoch=iepoch, is_random=is_random)
except:
test_info = None
try: # collect results on the proposed validation set
valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
except:
valid_info = None
try:
if dataset != 'cifar10':
valtest_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
else:
valtest_info = None
except:
valtest_info = None
if valid_info is not None:
xinfo['valid-loss'] = valid_info['loss']
xinfo['valid-accuracy'] = valid_info['accuracy']
xinfo['valid-per-time'] = valid_info['all_time'] / total if valid_info['all_time'] is not None else None
xinfo['valid-all-time'] = valid_info['all_time']
if test_info is not None:
xinfo['test-loss'] = test_info['loss']
xinfo['test-accuracy'] = test_info['accuracy']
xinfo['test-per-time'] = test_info['all_time'] / total if test_info['all_time'] is not None else None
xinfo['test-all-time'] = test_info['all_time']
if valtest_info is not None:
xinfo['valtest-loss'] = valtest_info['loss']
xinfo['valtest-accuracy'] = valtest_info['accuracy']
xinfo['valtest-per-time'] = valtest_info['all_time'] / total if valtest_info['all_time'] is not None else None
xinfo['valtest-all-time'] = valtest_info['all_time']
return xinfo
def show(self, index: int = -1) -> None:
"""This function will print the information of a specific (or all) architecture(s)."""
self._show(index, print_information)
@staticmethod
def str2lists(arch_str: Text) -> List[tuple]:
"""
This function shows how to read the string-based architecture encoding.
It is the same as the `str2structure` func in `AutoDL-Projects/lib/models/cell_searchs/genotypes.py`
:param
arch_str: the input is a string indicates the architecture topology, such as
|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|
:return: a list of tuple, contains multiple (op, input_node_index) pairs.
:usage
arch = api.str2lists( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
print ('there are {:} nodes in this arch'.format(len(arch)+1)) # arch is a list
for i, node in enumerate(arch):
print('the {:}-th node is the sum of these {:} nodes with op: {:}'.format(i+1, len(node), node))
"""
node_strs = arch_str.split('+')
genotypes = []
for i, node_str in enumerate(node_strs):
inputs = list(filter(lambda x: x != '', node_str.split('|')))
for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
inputs = ( xi.split('~') for xi in inputs )
input_infos = tuple( (op, int(IDX)) for (op, IDX) in inputs)
genotypes.append( input_infos )
return genotypes
@staticmethod
def str2matrix(arch_str: Text,
search_space: List[Text] = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']) -> np.ndarray:
"""
This func shows how to convert the string-based architecture encoding to the encoding strategy in NAS-Bench-101.
:param
arch_str: the input is a string indicates the architecture topology, such as
|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|
search_space: a list of operation string, the default list is the topology search space for NATS-BENCH.
the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/models/cell_operations.py#L24
:return
the numpy matrix (2-D np.ndarray) representing the DAG of this architecture topology
:usage
matrix = api.str2matrix( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
This matrix is 4-by-4 matrix representing a cell with 4 nodes (only the lower left triangle is useful).
[ [0, 0, 0, 0], # the first line represents the input (0-th) node
[2, 0, 0, 0], # the second line represents the 1-st node, is calculated by 2-th-op( 0-th-node )
[0, 0, 0, 0], # the third line represents the 2-nd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node )
[0, 0, 1, 0] ] # the fourth line represents the 3-rd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node ) + 1-th-op( 2-th-node )
In the topology search space in NATS-BENCH, 0-th-op is 'none', 1-th-op is 'skip_connect',
2-th-op is 'nor_conv_1x1', 3-th-op is 'nor_conv_3x3', 4-th-op is 'avg_pool_3x3'.
:(NOTE)
If a node has two input-edges from the same node, this function does not work. One edge will be overlapped.
"""
node_strs = arch_str.split('+')
num_nodes = len(node_strs) + 1
matrix = np.zeros((num_nodes, num_nodes))
for i, node_str in enumerate(node_strs):
inputs = list(filter(lambda x: x != '', node_str.split('|')))
for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
for xi in inputs:
op, idx = xi.split('~')
if op not in search_space: raise ValueError('this op ({:}) is not in {:}'.format(op, search_space))
op_idx, node_idx = search_space.index(op), int(idx)
matrix[i+1, node_idx] = op_idx
return matrix

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
############################################################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size
############################################################################################
# In this Python file, we define NASBenchMetaAPI, the abstract class for benchmark APIs.
# We also define the class ArchResults, which contains all information of a single architecture trained by one kind of hyper-parameters on three datasets.
# We also define the class ResultsCount, which contains all information of a single trial for a single architecture.
############################################################################################
# History:
# [2020.07.31] The first version, where most content reused nas_201_api/api_utils.py
#
import os, abc, copy, random, torch, numpy as np
from pathlib import Path
from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
def remap_dataset_set_names(dataset, metric_on_set, verbose=False):
"""re-map the metric_on_set to internal keys"""
if verbose:
print('Call internal function _remap_dataset_set_names with dataset={:} and metric_on_set={:}'.format(dataset, metric_on_set))
if dataset == 'cifar10' and metric_on_set == 'valid':
dataset, metric_on_set = 'cifar10-valid', 'x-valid'
elif dataset == 'cifar10' and metric_on_set == 'test':
dataset, metric_on_set = 'cifar10', 'ori-test'
elif dataset == 'cifar10' and metric_on_set == 'train':
dataset, metric_on_set = 'cifar10', 'train'
elif (dataset == 'cifar100' or dataset == 'ImageNet16-120') and metric_on_set == 'valid':
metric_on_set = 'x-valid'
elif (dataset == 'cifar100' or dataset == 'ImageNet16-120') and metric_on_set == 'test':
metric_on_set = 'x-test'
if verbose:
print(' return dataset={:} and metric_on_set={:}'.format(dataset, metric_on_set))
return dataset, metric_on_set
class NASBenchMetaAPI(metaclass=abc.ABCMeta):
@abc.abstractmethod
def __init__(self, file_path_or_dict: Optional[Union[Text, Dict]]=None, verbose: bool=True):
"""The initialization function that takes the dataset file path (or a dict loaded from that path) as input."""
def __getitem__(self, index: int):
return copy.deepcopy(self.meta_archs[index])
def arch(self, index: int):
"""Return the topology structure of the `index`-th architecture."""
if self.verbose:
print('Call the arch function with index={:}'.format(index))
assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs))
return copy.deepcopy(self.meta_archs[index])
def __len__(self):
return len(self.meta_archs)
def __repr__(self):
return ('{name}({num}/{total} architectures, file={filename})'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs), filename=self.filename))
@property
def avaliable_hps(self):
return list(copy.deepcopy(self._avaliable_hps))
@property
def used_time(self):
return self._used_time
@property
def search_space_name(self):
return self._search_space_name
def reset_time(self):
self._used_time = 0
def simulate_train_eval(self, arch, dataset, iepoch=None, hp='12', account_time=True):
index = self.query_index_by_arch(arch)
all_names = ('cifar10', 'cifar100', 'ImageNet16-120')
assert dataset in all_names, 'Invalid dataset name : {:} vs {:}'.format(dataset, all_names)
if dataset == 'cifar10':
info = self.get_more_info(index, 'cifar10-valid', iepoch=iepoch, hp=hp, is_random=True)
else:
info = self.get_more_info(index, dataset, iepoch=iepoch, hp=hp, is_random=True)
valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
latency = self.get_latency(index, dataset)
if account_time:
self._used_time += time_cost
return valid_acc, latency, time_cost, self._used_time
def random(self):
"""Return a random index of all architectures."""
return random.randint(0, len(self.meta_archs)-1)
def query_index_by_arch(self, arch):
""" This function is used to query the index of an architecture in the search space.
In the topology search space, the input arch can be an architecture string such as '|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|';
or an instance that has the 'tostr' function that can generate the architecture string;
or it is directly an architecture index, in this case, we will check whether it is valid or not.
This function will return the index.
If return -1, it means this architecture is not in the search space.
Otherwise, it will return an int in [0, the-number-of-candidates-in-the-search-space).
"""
if self.verbose:
print('Call query_index_by_arch with arch={:}'.format(arch))
if isinstance(arch, int):
if 0 <= arch < len(self):
return arch
else:
raise ValueError('Invalid architecture index {:} vs [{:}, {:}].'.format(arch, 0, len(self)))
elif isinstance(arch, str):
if arch in self.archstr2index: arch_index = self.archstr2index[ arch ]
else : arch_index = -1
elif hasattr(arch, 'tostr'):
if arch.tostr() in self.archstr2index: arch_index = self.archstr2index[ arch.tostr() ]
else : arch_index = -1
else: arch_index = -1
return arch_index
def query_by_arch(self, arch, hp):
# This is to make the current version be compatible with the old version.
return self.query_info_str_by_arch(arch, hp)
@abc.abstractmethod
def reload(self, archive_root: Text = None, index: int = None):
"""Overwrite all information of the 'index'-th architecture in the search space, where the data will be loaded from 'archive_root'.
If index is None, overwrite all ckps.
"""
def clear_params(self, index: int, hp: Optional[Text]=None):
"""Remove the architecture's weights to save memory.
:arg
index: the index of the target architecture
hp: a flag to controll how to clear the parameters.
-- None: clear all the weights in '01'/'12'/'90', which indicates the number of training epochs.
-- '01' or '12' or '90': clear all the weights in arch2infos_dict[index][hp].
"""
if self.verbose:
print('Call clear_params with index={:} and hp={:}'.format(index, hp))
if hp is None:
for key, result in self.arch2infos_dict[index].items():
result.clear_params()
else:
if str(hp) not in self.arch2infos_dict[index]:
raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(index, list(self.arch2infos_dict[index].keys()), hp))
self.arch2infos_dict[index][str(hp)].clear_params()
@abc.abstractmethod
def query_info_str_by_arch(self, arch, hp: Text='12'):
"""This function is used to query the information of a specific architecture."""
def _query_info_str_by_arch(self, arch, hp: Text='12', print_information=None):
arch_index = self.query_index_by_arch(arch)
if arch_index in self.arch2infos_dict:
if hp not in self.arch2infos_dict[arch_index]:
raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(index, list(self.arch2infos_dict[arch_index].keys()), hp))
info = self.arch2infos_dict[arch_index][hp]
strings = print_information(info, 'arch-index={:}'.format(arch_index))
return '\n'.join(strings)
else:
print ('Find this arch-index : {:}, but this arch is not evaluated.'.format(arch_index))
return None
def query_meta_info_by_index(self, arch_index, hp: Text = '12'):
"""Return the ArchResults for the 'arch_index'-th architecture. This function is similar to query_by_index."""
if self.verbose:
print('Call query_meta_info_by_index with arch_index={:}, hp={:}'.format(arch_index, hp))
if arch_index in self.arch2infos_dict:
if hp not in self.arch2infos_dict[arch_index]:
raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(arch_index, list(self.arch2infos_dict[arch_index].keys()), hp))
info = self.arch2infos_dict[arch_index][hp]
else:
raise ValueError('arch_index [{:}] does not in arch2infos'.format(arch_index))
return copy.deepcopy(info)
def query_by_index(self, arch_index: int, dataname: Union[None, Text] = None, hp: Text = '12'):
""" This 'query_by_index' function is used to query information with the training of 01 epochs, 12 epochs, 90 epochs, or 200 epochs.
------
If hp=01, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/01E.config)
If hp=12, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/12E.config)
If hp=90, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/90E.config)
If hp=200, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/200E.config)
------
If dataname is None, return the ArchResults
else, return a dict with all trials on that dataset (the key is the seed)
Options are 'cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120'.
-- cifar10-valid : training the model on the CIFAR-10 training set.
-- cifar10 : training the model on the CIFAR-10 training + validation set.
-- cifar100 : training the model on the CIFAR-100 training set.
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
"""
if self.verbose:
print('Call query_by_index with arch_index={:}, dataname={:}, hp={:}'.format(arch_index, dataname, hp))
info = self.query_meta_info_by_index(arch_index, hp)
if dataname is None: return info
else:
if dataname not in info.get_dataset_names():
raise ValueError('invalid dataset-name : {:} vs. {:}'.format(dataname, info.get_dataset_names()))
return info.query(dataname)
def find_best(self, dataset, metric_on_set, FLOP_max=None, Param_max=None, hp: Text = '12'):
"""Find the architecture with the highest accuracy based on some constraints."""
if self.verbose:
print('Call find_best with dataset={:}, metric_on_set={:}, hp={:} | with #FLOPs < {:} and #Params < {:}'.format(dataset, metric_on_set, hp, FLOP_max, Param_max))
dataset, metric_on_set = remap_dataset_set_names(dataset, metric_on_set, self.verbose)
best_index, highest_accuracy = -1, None
for i, arch_index in enumerate(self.evaluated_indexes):
arch_info = self.arch2infos_dict[arch_index][hp]
info = arch_info.get_compute_costs(dataset) # the information of costs
flop, param, latency = info['flops'], info['params'], info['latency']
if FLOP_max is not None and flop > FLOP_max : continue
if Param_max is not None and param > Param_max: continue
xinfo = arch_info.get_metrics(dataset, metric_on_set) # the information of loss and accuracy
loss, accuracy = xinfo['loss'], xinfo['accuracy']
if best_index == -1:
best_index, highest_accuracy = arch_index, accuracy
elif highest_accuracy < accuracy:
best_index, highest_accuracy = arch_index, accuracy
if self.verbose:
print(' the best architecture : [{:}] {:} with accuracy={:.3f}%'.format(best_index, self.arch(best_index), highest_accuracy))
return best_index, highest_accuracy
def get_net_param(self, index, dataset, seed: Optional[int], hp: Text = '12'):
"""
This function is used to obtain the trained weights of the `index`-th architecture on `dataset` with the seed of `seed`
Args [seed]:
-- None : return a dict containing the trained weights of all trials, where each key is a seed and its corresponding value is the weights.
-- a interger : return the weights of a specific trial, whose seed is this interger.
Args [hp]:
-- 01 : train the model by 01 epochs
-- 12 : train the model by 12 epochs
-- 90 : train the model by 90 epochs
-- 200 : train the model by 200 epochs
"""
if self.verbose:
print('Call the get_net_param function with index={:}, dataset={:}, seed={:}, hp={:}'.format(index, dataset, seed, hp))
info = self.query_meta_info_by_index(index, hp)
return info.get_net_param(dataset, seed)
def get_net_config(self, index: int, dataset: Text):
"""
This function is used to obtain the configuration for the `index`-th architecture on `dataset`.
Args [dataset] (4 possible options):
-- cifar10-valid : training the model on the CIFAR-10 training set.
-- cifar10 : training the model on the CIFAR-10 training + validation set.
-- cifar100 : training the model on the CIFAR-100 training set.
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
This function will return a dict.
========= Some examlpes for using this function:
config = api.get_net_config(128, 'cifar10')
"""
if self.verbose:
print('Call the get_net_config function with index={:}, dataset={:}.'.format(index, dataset))
if index in self.arch2infos_dict:
info = self.arch2infos_dict[index]
else:
raise ValueError('The arch_index={:} is not in arch2infos_dict.'.format(arch_index))
info = next(iter(info.values()))
results = info.query(dataset, None)
results = next(iter(results.values()))
return results.get_config(None)
def get_cost_info(self, index: int, dataset: Text, hp: Text = '12') -> Dict[Text, float]:
"""To obtain the cost metric for the `index`-th architecture on a dataset."""
if self.verbose:
print('Call the get_cost_info function with index={:}, dataset={:}, and hp={:}.'.format(index, dataset, hp))
info = self.query_meta_info_by_index(index, hp)
return info.get_compute_costs(dataset)
def get_latency(self, index: int, dataset: Text, hp: Text = '12') -> float:
"""
To obtain the latency of the network (by default it will return the latency with the batch size of 256).
:param index: the index of the target architecture
:param dataset: the dataset name (cifar10-valid, cifar10, cifar100, ImageNet16-120)
:return: return a float value in seconds
"""
if self.verbose:
print('Call the get_latency function with index={:}, dataset={:}, and hp={:}.'.format(index, dataset, hp))
cost_dict = self.get_cost_info(index, dataset, hp)
return cost_dict['latency']
@abc.abstractmethod
def show(self, index=-1):
"""This function will print the information of a specific (or all) architecture(s)."""
def _show(self, index=-1, print_information=None) -> None:
"""
This function will print the information of a specific (or all) architecture(s).
:param index: If the index < 0: it will loop for all architectures and print their information one by one.
else: it will print the information of the 'index'-th architecture.
:return: nothing
"""
if index < 0: # show all architectures
print(self)
for i, idx in enumerate(self.evaluated_indexes):
print('\n' + '-' * 10 + ' The ({:5d}/{:5d}) {:06d}-th architecture! '.format(i, len(self.evaluated_indexes), idx) + '-'*10)
print('arch : {:}'.format(self.meta_archs[idx]))
for key, result in self.arch2infos_dict[index].items():
strings = print_information(result)
print('>' * 40 + ' {:03d} epochs '.format(result.get_total_epoch()) + '>' * 40)
print('\n'.join(strings))
print('<' * 40 + '------------' + '<' * 40)
else:
if 0 <= index < len(self.meta_archs):
if index not in self.evaluated_indexes: print('The {:}-th architecture has not been evaluated or not saved.'.format(index))
else:
arch_info = self.arch2infos_dict[index]
for key, result in self.arch2infos_dict[index].items():
strings = print_information(result)
print('>' * 40 + ' {:03d} epochs '.format(result.get_total_epoch()) + '>' * 40)
print('\n'.join(strings))
print('<' * 40 + '------------' + '<' * 40)
else:
print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs)))
def statistics(self, dataset: Text, hp: Union[Text, int]) -> Dict[int, int]:
"""This function will count the number of total trials."""
if self.verbose:
print('Call the statistics function with dataset={:} and hp={:}.'.format(dataset, hp))
valid_datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
if dataset not in valid_datasets:
raise ValueError('{:} not in {:}'.format(dataset, valid_datasets))
nums, hp = defaultdict(lambda: 0), str(hp)
for index in range(len(self)):
archInfo = self.arch2infos_dict[index][hp]
dataset_seed = archInfo.dataset_seed
if dataset not in dataset_seed:
nums[0] += 1
else:
nums[len(dataset_seed[dataset])] += 1
return dict(nums)
class ArchResults(object):
def __init__(self, arch_index, arch_str):
self.arch_index = int(arch_index)
self.arch_str = copy.deepcopy(arch_str)
self.all_results = dict()
self.dataset_seed = dict()
self.clear_net_done = False
def get_compute_costs(self, dataset):
x_seeds = self.dataset_seed[dataset]
results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
flops = [result.flop for result in results]
params = [result.params for result in results]
latencies = [result.get_latency() for result in results]
latencies = [x for x in latencies if x > 0]
mean_latency = np.mean(latencies) if len(latencies) > 0 else None
time_infos = defaultdict(list)
for result in results:
time_info = result.get_times()
for key, value in time_info.items(): time_infos[key].append( value )
info = {'flops' : np.mean(flops),
'params' : np.mean(params),
'latency': mean_latency}
for key, value in time_infos.items():
if len(value) > 0 and value[0] is not None:
info[key] = np.mean(value)
else: info[key] = None
return info
def get_metrics(self, dataset, setname, iepoch=None, is_random=False):
"""
This `get_metrics` function is used to obtain obtain the loss, accuracy, etc information on a specific dataset.
If not specify, each set refer to the proposed split in NAS-Bench-201 paper.
If some args return None or raise error, then it is not avaliable.
========================================
Args [dataset] (4 possible options):
-- cifar10-valid : training the model on the CIFAR-10 training set.
-- cifar10 : training the model on the CIFAR-10 training + validation set.
-- cifar100 : training the model on the CIFAR-100 training set.
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
Args [setname] (each dataset has different setnames):
-- When dataset = cifar10-valid, you can use 'train', 'x-valid', 'ori-test'
------ 'train' : the metric on the training set.
------ 'x-valid' : the metric on the validation set.
------ 'ori-test' : the metric on the test set.
-- When dataset = cifar10, you can use 'train', 'ori-test'.
------ 'train' : the metric on the training + validation set.
------ 'ori-test' : the metric on the test set.
-- When dataset = cifar100 or ImageNet16-120, you can use 'train', 'ori-test', 'x-valid', 'x-test'
------ 'train' : the metric on the training set.
------ 'x-valid' : the metric on the validation set.
------ 'x-test' : the metric on the test set.
------ 'ori-test' : the metric on the validation + test set.
Args [iepoch] (None or an integer in [0, the-number-of-total-training-epochs)
------ None : return the metric after the last training epoch.
------ an integer i : return the metric after the i-th training epoch.
Args [is_random]:
------ True : return the metric of a randomly selected trial.
------ False : return the averaged metric of all avaliable trials.
------ an integer indicating the 'seed' value : return the metric of a specific trial (whose random seed is 'is_random').
"""
x_seeds = self.dataset_seed[dataset]
results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
infos = defaultdict(list)
for result in results:
if setname == 'train':
info = result.get_train(iepoch)
else:
info = result.get_eval(setname, iepoch)
for key, value in info.items(): infos[key].append( value )
return_info = dict()
if isinstance(is_random, bool) and is_random: # randomly select one
index = random.randint(0, len(results)-1)
for key, value in infos.items(): return_info[key] = value[index]
elif isinstance(is_random, bool) and not is_random: # average
for key, value in infos.items():
if len(value) > 0 and value[0] is not None:
return_info[key] = np.mean(value)
else: return_info[key] = None
elif isinstance(is_random, int): # specify the seed
if is_random not in x_seeds: raise ValueError('can not find random seed ({:}) from {:}'.format(is_random, x_seeds))
index = x_seeds.index(is_random)
for key, value in infos.items(): return_info[key] = value[index]
else:
raise ValueError('invalid value for is_random: {:}'.format(is_random))
return return_info
def show(self, is_print=False):
return print_information(self, None, is_print)
def get_dataset_names(self):
return list(self.dataset_seed.keys())
def get_dataset_seeds(self, dataset):
return copy.deepcopy( self.dataset_seed[dataset] )
def get_net_param(self, dataset: Text, seed: Union[None, int] =None):
"""
This function will return the trained network's weights on the 'dataset'.
:arg
dataset: one of 'cifar10-valid', 'cifar10', 'cifar100', and 'ImageNet16-120'.
seed: an integer indicates the seed value or None that indicates returing all trials.
"""
if seed is None:
x_seeds = self.dataset_seed[dataset]
return {seed: self.all_results[(dataset, seed)].get_net_param() for seed in x_seeds}
else:
xkey = (dataset, seed)
if xkey in self.all_results:
return self.all_results[xkey].get_net_param()
else:
raise ValueError('key={:} not in {:}'.format(xkey, list(self.all_results.keys())))
def reset_latency(self, dataset: Text, seed: Union[None, Text], latency: float) -> None:
"""This function is used to reset the latency in all corresponding ResultsCount(s)."""
if seed is None:
for seed in self.dataset_seed[dataset]:
self.all_results[(dataset, seed)].update_latency([latency])
else:
self.all_results[(dataset, seed)].update_latency([latency])
def reset_pseudo_train_times(self, dataset: Text, seed: Union[None, Text], estimated_per_epoch_time: float) -> None:
"""This function is used to reset the train-times in all corresponding ResultsCount(s)."""
if seed is None:
for seed in self.dataset_seed[dataset]:
self.all_results[(dataset, seed)].reset_pseudo_train_times(estimated_per_epoch_time)
else:
self.all_results[(dataset, seed)].reset_pseudo_train_times(estimated_per_epoch_time)
def reset_pseudo_eval_times(self, dataset: Text, seed: Union[None, Text], eval_name: Text, estimated_per_epoch_time: float) -> None:
"""This function is used to reset the eval-times in all corresponding ResultsCount(s)."""
if seed is None:
for seed in self.dataset_seed[dataset]:
self.all_results[(dataset, seed)].reset_pseudo_eval_times(eval_name, estimated_per_epoch_time)
else:
self.all_results[(dataset, seed)].reset_pseudo_eval_times(eval_name, estimated_per_epoch_time)
def get_latency(self, dataset: Text) -> float:
"""Get the latency of a model on the target dataset. [Timestamp: 2020.03.09]"""
latencies = []
for seed in self.dataset_seed[dataset]:
latency = self.all_results[(dataset, seed)].get_latency()
if not isinstance(latency, float) or latency <= 0:
raise ValueError('invalid latency of {:} with seed={:} : {:}'.format(dataset, seed, latency))
latencies.append(latency)
return sum(latencies) / len(latencies)
def get_total_epoch(self, dataset=None):
"""Return the total number of training epochs."""
if dataset is None:
epochss = []
for xdata, x_seeds in self.dataset_seed.items():
epochss += [self.all_results[(xdata, seed)].get_total_epoch() for seed in x_seeds]
elif isinstance(dataset, str):
x_seeds = self.dataset_seed[dataset]
epochss = [self.all_results[(dataset, seed)].get_total_epoch() for seed in x_seeds]
else:
raise ValueError('invalid dataset={:}'.format(dataset))
if len(set(epochss)) > 1: raise ValueError('Each trial mush have the same number of training epochs : {:}'.format(epochss))
return epochss[-1]
def query(self, dataset, seed=None):
"""Return the ResultsCount object (containing all information of a single trial) for 'dataset' and 'seed'"""
if seed is None:
x_seeds = self.dataset_seed[dataset]
return {seed: self.all_results[(dataset, seed)] for seed in x_seeds}
else:
return self.all_results[(dataset, seed)]
def arch_idx_str(self):
return '{:06d}'.format(self.arch_index)
def update(self, dataset_name, seed, result):
if dataset_name not in self.dataset_seed:
self.dataset_seed[dataset_name] = []
assert seed not in self.dataset_seed[dataset_name], '{:}-th arch alreadly has this seed ({:}) on {:}'.format(self.arch_index, seed, dataset_name)
self.dataset_seed[ dataset_name ].append( seed )
self.dataset_seed[ dataset_name ] = sorted( self.dataset_seed[ dataset_name ] )
assert (dataset_name, seed) not in self.all_results
self.all_results[ (dataset_name, seed) ] = result
self.clear_net_done = False
def state_dict(self):
state_dict = dict()
for key, value in self.__dict__.items():
if key == 'all_results': # contain the class of ResultsCount
xvalue = dict()
assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value))
for _k, _v in value.items():
assert isinstance(_v, ResultsCount), 'invalid type of value for {:}/{:} : {:}'.format(key, _k, type(_v))
xvalue[_k] = _v.state_dict()
else:
xvalue = value
state_dict[key] = xvalue
return state_dict
def load_state_dict(self, state_dict):
new_state_dict = dict()
for key, value in state_dict.items():
if key == 'all_results': # to convert to the class of ResultsCount
xvalue = dict()
assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value))
for _k, _v in value.items():
xvalue[_k] = ResultsCount.create_from_state_dict(_v)
else: xvalue = value
new_state_dict[key] = xvalue
self.__dict__.update(new_state_dict)
@staticmethod
def create_from_state_dict(state_dict_or_file):
x = ArchResults(-1, -1)
if isinstance(state_dict_or_file, str): # a file path
state_dict = torch.load(state_dict_or_file, map_location='cpu')
elif isinstance(state_dict_or_file, dict):
state_dict = state_dict_or_file
else:
raise ValueError('invalid type of state_dict_or_file : {:}'.format(type(state_dict_or_file)))
x.load_state_dict(state_dict)
return x
# This function is used to clear the weights saved in each 'result'
# This can help reduce the memory footprint.
def clear_params(self):
for key, result in self.all_results.items():
del result.net_state_dict
result.net_state_dict = None
self.clear_net_done = True
def debug_test(self):
"""This function is used for me to debug and test, which will call most methods."""
all_dataset = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
for dataset in all_dataset:
print('---->>>> {:}'.format(dataset))
print('The latency on {:} is {:} s'.format(dataset, self.get_latency(dataset)))
for seed in self.dataset_seed[dataset]:
result = self.all_results[(dataset, seed)]
print(' ==>> result = {:}'.format(result))
print(' ==>> cost = {:}'.format(result.get_times()))
def __repr__(self):
return ('{name}(arch-index={index}, arch={arch}, {num} runs, clear={clear})'.format(name=self.__class__.__name__, index=self.arch_index, arch=self.arch_str, num=len(self.all_results), clear=self.clear_net_done))
"""
This class (ResultsCount) is used to save the information of one trial for a single architecture.
I did not write much comment for this class, because it is the lowest-level class in NAS-Bench-201 API, which will be rarely called.
If you have any question regarding this class, please open an issue or email me.
"""
class ResultsCount(object):
def __init__(self, name, state_dict, train_accs, train_losses, params, flop, arch_config, seed, epochs, latency):
self.name = name
self.net_state_dict = state_dict
self.train_acc1es = copy.deepcopy(train_accs)
self.train_acc5es = None
self.train_losses = copy.deepcopy(train_losses)
self.train_times = None
self.arch_config = copy.deepcopy(arch_config)
self.params = params
self.flop = flop
self.seed = seed
self.epochs = epochs
self.latency = latency
# evaluation results
self.reset_eval()
def update_train_info(self, train_acc1es, train_acc5es, train_losses, train_times) -> None:
self.train_acc1es = train_acc1es
self.train_acc5es = train_acc5es
self.train_losses = train_losses
self.train_times = train_times
def reset_pseudo_train_times(self, estimated_per_epoch_time: float) -> None:
"""Assign the training times."""
train_times = OrderedDict()
for i in range(self.epochs):
train_times[i] = estimated_per_epoch_time
self.train_times = train_times
def reset_pseudo_eval_times(self, eval_name: Text, estimated_per_epoch_time: float) -> None:
"""Assign the evaluation times."""
if eval_name not in self.eval_names: raise ValueError('invalid eval name : {:}'.format(eval_name))
for i in range(self.epochs):
self.eval_times['{:}@{:}'.format(eval_name,i)] = estimated_per_epoch_time
def reset_eval(self):
self.eval_names = []
self.eval_acc1es = {}
self.eval_times = {}
self.eval_losses = {}
def update_latency(self, latency):
self.latency = copy.deepcopy( latency )
def get_latency(self) -> float:
"""Return the latency value in seconds. -1 represents not avaliable ; otherwise it should be a float value"""
if self.latency is None: return -1.0
else: return sum(self.latency) / len(self.latency)
def update_eval(self, accs, losses, times): # new version
data_names = set([x.split('@')[0] for x in accs.keys()])
for data_name in data_names:
assert data_name not in self.eval_names, '{:} has already been added into eval-names'.format(data_name)
self.eval_names.append( data_name )
for iepoch in range(self.epochs):
xkey = '{:}@{:}'.format(data_name, iepoch)
self.eval_acc1es[ xkey ] = accs[ xkey ]
self.eval_losses[ xkey ] = losses[ xkey ]
self.eval_times [ xkey ] = times[ xkey ]
def update_OLD_eval(self, name, accs, losses): # old version
assert name not in self.eval_names, '{:} has already added'.format(name)
self.eval_names.append( name )
for iepoch in range(self.epochs):
if iepoch in accs:
self.eval_acc1es['{:}@{:}'.format(name,iepoch)] = accs[iepoch]
self.eval_losses['{:}@{:}'.format(name,iepoch)] = losses[iepoch]
def __repr__(self):
num_eval = len(self.eval_names)
set_name = '[' + ', '.join(self.eval_names) + ']'
return ('{name}({xname}, arch={arch}, FLOP={flop:.2f}M, Param={param:.3f}MB, seed={seed}, {num_eval} eval-sets: {set_name})'.format(name=self.__class__.__name__, xname=self.name, arch=self.arch_config['arch_str'], flop=self.flop, param=self.params, seed=self.seed, num_eval=num_eval, set_name=set_name))
def get_total_epoch(self):
return copy.deepcopy(self.epochs)
def get_times(self):
"""Obtain the information regarding both training and evaluation time."""
if self.train_times is not None and isinstance(self.train_times, dict):
train_times = list( self.train_times.values() )
time_info = {'T-train@epoch': np.mean(train_times), 'T-train@total': np.sum(train_times)}
else:
time_info = {'T-train@epoch': None, 'T-train@total': None }
for name in self.eval_names:
try:
xtimes = [self.eval_times['{:}@{:}'.format(name,i)] for i in range(self.epochs)]
time_info['T-{:}@epoch'.format(name)] = np.mean(xtimes)
time_info['T-{:}@total'.format(name)] = np.sum(xtimes)
except:
time_info['T-{:}@epoch'.format(name)] = None
time_info['T-{:}@total'.format(name)] = None
return time_info
def get_eval_set(self):
return self.eval_names
# get the training information
def get_train(self, iepoch=None):
if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
if self.train_times is not None:
xtime = self.train_times[iepoch]
atime = sum([self.train_times[i] for i in range(iepoch+1)])
else: xtime, atime = None, None
return {'iepoch' : iepoch,
'loss' : self.train_losses[iepoch],
'accuracy': self.train_acc1es[iepoch],
'cur_time': xtime,
'all_time': atime}
def get_eval(self, name, iepoch=None):
"""Get the evaluation information ; there could be multiple evaluation sets (identified by the 'name' argument)."""
if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
def _internal_query(xname):
if isinstance(self.eval_times,dict) and len(self.eval_times) > 0:
xtime = self.eval_times['{:}@{:}'.format(xname, iepoch)]
atime = sum([self.eval_times['{:}@{:}'.format(xname, i)] for i in range(iepoch+1)])
else:
xtime, atime = None, None
return {'iepoch' : iepoch,
'loss' : self.eval_losses['{:}@{:}'.format(xname, iepoch)],
'accuracy': self.eval_acc1es['{:}@{:}'.format(xname, iepoch)],
'cur_time': xtime,
'all_time': atime}
if name == 'valid':
return _internal_query('x-valid')
else:
return _internal_query(name)
def get_net_param(self, clone=False):
if clone: return copy.deepcopy(self.net_state_dict)
else: return self.net_state_dict
def get_config(self, str2structure):
"""This function is used to obtain the config dict for this architecture."""
if str2structure is None:
# In this case, this is architecture in the size search space of NATS-BENCH.
if 'name' in self.arch_config and self.arch_config['name'] == 'infer.shape.tiny':
return {'name': 'infer.shape.tiny', 'channels': self.arch_config['channels'],
'genotype': self.arch_config['genotype'], 'num_classes': self.arch_config['class_num']}
# In this case, this is architecture in the topology search space of NATS-BENCH.
else:
return {'name': 'infer.tiny', 'C': self.arch_config['channel'],
'N' : self.arch_config['num_cells'],
'arch_str': self.arch_config['arch_str'], 'num_classes': self.arch_config['class_num']}
else:
# In this case, this is architecture in the size search space of NATS-BENCH.
if 'name' in self.arch_config and self.arch_config['name'] == 'infer.shape.tiny':
return {'name': 'infer.shape.tiny', 'channels': self.arch_config['channels'],
'genotype': str2structure(self.arch_config['genotype']), 'num_classes': self.arch_config['class_num']}
# In this case, this is architecture in the topology search space of NATS-BENCH.
else:
return {'name': 'infer.tiny', 'C': self.arch_config['channel'],
'N' : self.arch_config['num_cells'],
'genotype': str2structure(self.arch_config['arch_str']), 'num_classes': self.arch_config['class_num']}
def state_dict(self):
_state_dict = {key: value for key, value in self.__dict__.items()}
return _state_dict
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
@staticmethod
def create_from_state_dict(state_dict):
x = ResultsCount(None, None, None, None, None, None, None, None, None, None)
x.load_state_dict(state_dict)
return x