class HierarchicalDartsIntegrationTest(unittest.TestCase):
def setUp(self):
utils.set_seed(1)
self.optimizer = DARTSOptimizer(config)
self.optimizer.adapt_search_space(HierarchicalSearchSpace())
self.optimizer.before_training()
def test_update(self):
stats = self.optimizer.step(data_train, data_val)
self.assertTrue(len(stats) == 4)
self.assertAlmostEqual(stats[2].detach().cpu().numpy(),
2.4094,
places=3)
self.assertAlmostEqual(stats[3].detach().cpu().numpy(),
2.4094,
places=3)
def test_feed_forward(self):
final_arch = self.optimizer.get_final_architecture()
logits = final_arch(data_train[0])
self.assertTrue(logits.shape == (2, 10))
self.assertAlmostEqual(logits[0, 0].detach().cpu().numpy(),
-0.0545,
places=3)
def setUp(self):
utils.set_seed(1)
self.optimizer = DARTSOptimizer(config)
self.optimizer.adapt_search_space(HierarchicalSearchSpace())
self.optimizer.before_training()
HierarchicalSearchSpace,
)
from naslib.utils import utils, setup_logger
# Read args and config, setup logger
config = utils.get_config_from_args()
utils.set_seed(config.seed)
logger = setup_logger(config.save + "/log.log")
logger.setLevel(logging.INFO) # default DEBUG is too verbose
utils.log_args(config)
supported_optimizers = {
'darts': DARTSOptimizer(config.search),
'gdas': GDASOptimizer(config.search),
'random': RandomSearch(sample_size=1),
#'re': RegularizedEvolution(config.search),
}
# search_space = SimpleCellSearchSpace()
search_space = NasBench201SeachSpace()
# search_space = HierarchicalSearchSpace()
# search_space = DartsSearchSpace()
assert search_space.QUERYABLE
optimizer = supported_optimizers[config.optimizer]
optimizer.adapt_search_space(search_space)
OneShotNASOptimizer, RandomNASOptimizer, RandomSearch, \
RegularizedEvolution, LocalSearch, Bananas, BasePredictor
from naslib.search_spaces import NasBench201SearchSpace
from naslib.utils import utils, setup_logger, get_dataset_api
config = utils.get_config_from_args()
utils.set_seed(config.seed)
logger = setup_logger(config.save + "/log.log")
logger.setLevel(logging.INFO) # default DEBUG is too verbose
utils.log_args(config)
supported_optimizers = {
'darts': DARTSOptimizer(config),
'gdas': GDASOptimizer(config),
'oneshot': OneShotNASOptimizer(config),
'rsws': RandomNASOptimizer(config),
're': RegularizedEvolution(config),
'rs': RandomSearch(config),
'ls': RandomSearch(config),
'bananas': Bananas(config),
'bp': BasePredictor(config)
}
search_space = NasBench201SearchSpace()
dataset_api = get_dataset_api('nasbench201', config.dataset)
optimizer = supported_optimizers[config.optimizer]
optimizer.adapt_search_space(search_space)
import logging from naslib.defaults.trainer import Trainer from naslib.optimizers import DARTSOptimizer, GDASOptimizer, RandomSearch from naslib.search_spaces import DartsSearchSpace, SimpleCellSearchSpace from naslib.utils import set_seed, setup_logger, get_config_from_args config = get_config_from_args() # use --help so see the options set_seed(config.seed) logger = setup_logger(config.save + "/log.log") logger.setLevel(logging.INFO) # default DEBUG is very verbose search_space = DartsSearchSpace() # use SimpleCellSearchSpace() for less heavy search optimizer = DARTSOptimizer(config) optimizer.adapt_search_space(search_space) trainer = Trainer(optimizer, config) trainer.search() # Search for an architecture trainer.evaluate() # Evaluate the best architecture
from naslib.search_spaces.hierarchical.graph import HierarchicalSearchSpace, LiuFinalArch
from naslib.optimizers.discrete.rs.optimizer import sample_random_architecture
config = utils.get_config_from_args()
utils.set_seed(config.seed)
logger = setup_logger(config.save + "/log.log")
logger.setLevel(logging.INFO) # default DEBUG is too verbose
utils.log_args(config)
search_space = HierarchicalSearchSpace()
best_arch = None
if config.optimizer == 'darts':
optimizer = DARTSOptimizer(config)
elif config.optimizer == 'gdas':
optimizer = GDASOptimizer(config)
elif config.optimizer == 'liu_et_al':
optimizer = DARTSOptimizer(
config) # hack to instanciate the trainer (is ignored during eval)
best_arch = LiuFinalArch()
elif config.optimizer == 'random':
optimizer = DARTSOptimizer(
config) # hack to instanciate the trainer (is ignored during eval)
best_arch = sample_random_architecture(search_space,
search_space.OPTIMIZER_SCOPE)
else:
raise ValueError("Unknown optimizer : {}".format(config.optimizer))
optimizer.adapt_search_space(search_space)
