def load_model(checkpoint_file):
checkpoint = torch.load(checkpoint_file)
args = checkpoint['args']
model = UnetModel(1, 1, args.num_chans, args.num_pools, args.drop_prob).to(args.device)
if args.data_parallel:
model = torch.nn.DataParallel(model)
model.load_state_dict(checkpoint['model'])
return model
def load_model(checkpoint_file):
checkpoint = torch.load(checkpoint_file)
args = checkpoint['args']
model = UnetModel(in_chans=1,
out_chans=1,
chans=args.num_chans,
num_pool_layers=args.num_pools,
drop_prob=args.drop_prob,
acceleration=args.accelerations,
center_fraction=args.center_fractions,
res=args.resolution).to(args.device)
if args.data_parallel:
model = torch.nn.DataParallel(model)
model.load_state_dict(checkpoint['model'])
return model