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)