def __init__(self, dataset: str) -> None:
"""Creates ProblemFactory for NASBench201.
Args:
dataset:
Accepts one of "cifar10", "cifar100" or "ImageNet16-120".
"""
self._dataset = dataset
if dataset == "cifar10":
self._dataset = "cifar10-valid" # Set name used in dataset API
self._dataset_api = get_dataset_api("nasbench201", dataset)
supported_optimizers = {
'bananas': Bananas(config),
'oneshot': OneShotNASOptimizer(config),
'rsws': RandomNASOptimizer(config),
}
supported_search_spaces = {
'nasbench101': NasBench101SearchSpace(),
'nasbench201': NasBench201SearchSpace(),
'darts': DartsSearchSpace()
}
#load_labeled = (True if config.search_space == 'darts' else False)
load_labeled = False
dataset_api = get_dataset_api(config.search_space, config.dataset)
utils.set_seed(config.seed)
search_space = supported_search_spaces[config.search_space]
optimizer = supported_optimizers[config.optimizer]
optimizer.adapt_search_space(search_space, dataset_api=dataset_api)
trainer = Trainer(optimizer, config, lightweight_output=True)
if config.optimizer == 'bananas':
trainer.search(resume_from="")
trainer.evaluate(resume_from="", dataset_api=dataset_api)
elif config.optimizer in ['oneshot', 'rsws']:
predictor = OneShotPredictor(config, trainer, model_path=config.model_path)
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)
trainer = Trainer(optimizer, config, lightweight_output=True)
#trainer.search()
#if not config.eval_only:
# checkpoint = utils.get_last_checkpoint(config) if config.resume else ""
# trainer.search(resume_from=checkpoint)
#checkpoint = utils.get_last_checkpoint(config, search=False) if config.resume else ""
#trainer.evaluate(resume_from=checkpoint, dataset_api=dataset_api)