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NAS-sharing-parameters support 3 datasets / update ops / update pypi

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D-X-Y
2020-01-11 00:19:58 +11:00
parent 96152a9904
commit c66afa4df8
17 changed files with 192 additions and 153 deletions
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32
exps/algos/DARTS-V1.py
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@@ -12,7 +12,7 @@ from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from datasets import get_datasets, SearchDataset
from datasets import get_datasets, get_nas_search_loaders
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
@@ -107,35 +107,7 @@ def main(xargs):
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
#config_path = 'configs/nas-benchmark/algos/DARTS.config'
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
if xargs.dataset == 'cifar10':
split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid # search over the proposed training and validation set
logger.log('Load split file from {:}'.format(split_Fpath)) # they are two disjoint groups in the original CIFAR-10 training set
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
# data loader
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data , batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
elif xargs.dataset == 'cifar100':
cifar100_test_split = load_config('configs/nas-benchmark/cifar100-test-split.txt', None, None)
search_train_data = train_data
search_valid_data = deepcopy(valid_data) ; search_valid_data.transform = train_data.transform
search_data = SearchDataset(xargs.dataset, [search_train_data,search_valid_data], list(range(len(search_train_data))), cifar100_test_split.xvalid)
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data , batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_test_split.xvalid), num_workers=xargs.workers, pin_memory=True)
elif xargs.dataset == 'ImageNet16-120':
imagenet_test_split = load_config('configs/nas-benchmark/imagenet-16-120-test-split.txt', None, None)
search_train_data = train_data
search_valid_data = deepcopy(valid_data) ; search_valid_data.transform = train_data.transform
search_data = SearchDataset(xargs.dataset, [search_train_data,search_valid_data], list(range(len(search_train_data))), imagenet_test_split.xvalid)
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data , batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_test_split.xvalid), num_workers=xargs.workers, pin_memory=True)
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))

24
exps/algos/DARTS-V2.py
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@@ -12,7 +12,7 @@ from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from datasets import get_datasets, SearchDataset
from datasets import get_datasets, get_nas_search_loaders
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
@@ -169,28 +169,8 @@ def main(xargs):
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
elif xargs.dataset.startswith('ImageNet16'):
split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
imagenet16_split = load_config(split_Fpath, None, None)
train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
#config_path = 'configs/nas-benchmark/algos/DARTS.config'
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
# data loader
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))

27
exps/algos/ENAS.py
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@@ -10,7 +10,7 @@ from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from datasets import get_datasets, SearchDataset
from datasets import get_datasets, get_nas_search_loaders
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
@@ -184,29 +184,14 @@ def main(xargs):
logger = prepare_logger(args)
train_data, test_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
elif xargs.dataset.startswith('ImageNet16'):
split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
imagenet16_split = load_config(split_Fpath, None, None)
train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
logger.log('use config from : {:}'.format(xargs.config_path))
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
logger.log('config: {:}'.format(config))
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = test_data.transform
valid_data = train_data_v2
_, train_loader, valid_loader = get_nas_search_loaders(train_data, test_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
# since ENAS will train the controller on valid-loader, we need to use train transformation for valid-loader
valid_loader.dataset.transform = deepcopy(train_loader.dataset.transform)
if hasattr(valid_loader.dataset, 'transforms'):
valid_loader.dataset.transforms = deepcopy(train_loader.dataset.transforms)
# data loader
train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split), num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))

23
exps/algos/GDAS.py
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@@ -12,7 +12,7 @@ from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config
from datasets import get_datasets, SearchDataset
from datasets import get_datasets, get_nas_search_loaders
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
@@ -80,25 +80,10 @@ def main(xargs):
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, _, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
elif xargs.dataset.startswith('ImageNet16'):
split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
imagenet16_split = load_config(split_Fpath, None, None)
train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
#config_path = 'configs/nas-benchmark/algos/GDAS.config'
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
# data loader
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
@@ -143,7 +128,7 @@ def main(xargs):
logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {}
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: search_model.genotype()}
# start training
start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup

29
exps/algos/RANDOM-NAS.py
View File

@@ -10,7 +10,7 @@ from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from datasets import get_datasets, SearchDataset
from datasets import get_datasets, get_nas_search_loaders
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
@@ -117,32 +117,9 @@ def main(xargs):
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
#elif xargs.dataset.startswith('ImageNet16'):
# # all_indexes = list(range(len(train_data))) ; random.seed(111) ; random.shuffle(all_indexes)
# # train_split, valid_split = sorted(all_indexes[: len(train_data)//2]), sorted(all_indexes[len(train_data)//2 :])
# # imagenet16_split = dict2config({'train': train_split, 'valid': valid_split}, None)
# # _ = configure2str(imagenet16_split, 'temp.txt')
# split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
# imagenet16_split = load_config(split_Fpath, None, None)
# train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
# logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
logger.log('config : {:}'.format(config))
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
# data loader
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.test_batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
(config.batch_size, config.test_batch_size), xargs.workers)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))

29
exps/algos/SETN.py
View File

@@ -12,7 +12,7 @@ from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from datasets import get_datasets, SearchDataset
from datasets import get_datasets, get_nas_search_loaders
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
@@ -135,29 +135,9 @@ def main(xargs):
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
elif xargs.dataset.startswith('ImageNet16'):
split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
imagenet16_split = load_config(split_Fpath, None, None)
train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
logger.log('Load split file from {:}'.format(split_Fpath))
else:
raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
#config_path = 'configs/nas-benchmark/algos/SETN.config'
config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
# data loader
search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.test_batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
(config.batch_size, config.test_batch_size), xargs.workers)
logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
@@ -202,7 +182,8 @@ def main(xargs):
logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {}
init_genotype, _ = get_best_arch(valid_loader, network, xargs.select_num)
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: init_genotype}
# start training
start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup