Python get_last_checkpoint Examples

Example #1

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)
trainer = Trainer(optimizer, config)

if config.eval_only and not best_arch:
    trainer.evaluate(resume_from=utils.get_last_checkpoint(
        config, search=False) if config.resume else "")
elif best_arch:
    best_arch.parse()
    trainer.evaluate(best_arch=best_arch)
else:
    trainer.search(
        resume_from=utils.get_last_checkpoint(config) if config.resume else "")
    trainer.evaluate(resume_from=utils.get_last_checkpoint(
        config, search=False) if config.resume else "")

Example #2

File: runner.py Project: jackyvan/NASLib

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),
    're': RegularizedEvolution(config),
    'rs': RandomSearch(config),
}

search_space = NasBench201SearchSpace()

optimizer = supported_optimizers[config.optimizer]
optimizer.adapt_search_space(search_space)

trainer = Trainer(optimizer, config)

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)