def cli(ctx, profile, snapshot):
# load hyper-parameters
hps = util.load_profile(profile)
util.manual_seed(hps.ablation.seed)
if snapshot is not None:
hps.general.warm_start = True
hps.general.pre_trained = snapshot
# build graph
builder = Builder(hps)
state = builder.build(training=False)
# load dataset
dataset = CelebA(root=hps.dataset.root,
transform=transforms.Compose(
(transforms.CenterCrop(160), transforms.Resize(128),
transforms.ToTensor())))
# start inference
inferer = Inferer(hps=hps,
graph=state['graph'],
devices=state['devices'],
data_device=state['data_device'])
ctx.obj['hps'] = hps
ctx.obj['dataset'] = dataset
ctx.obj['inferer'] = inferer
if __name__ == '__main__':
# this enables a Ctrl-C without triggering errors
signal.signal(signal.SIGINT, lambda x, y: sys.exit(0))
print("hello")
# parse arguments
#args = parse_args()
args = "profile/celeba.json"
# initialize logging
util.init_output_logging()
# load hyper-parameters
# hps = util.load_profile(args.profile)
hps = util.load_profile(args)
util.manual_seed(hps.ablation.seed)
# build graph
builder = Builder(hps)
state = builder.build()
# load dataset
dataset = CelebA(root=hps.dataset.root,
transform=transforms.Compose((
transforms.CenterCrop(160),
transforms.Resize(64),
transforms.ToTensor()
)))
# start training
trainer = Trainer(hps=hps, dataset=dataset, **state)
