def build_model(args):
model = define_Gen(input_nc=1,
output_nc=1,
ngf=args.num_chans,
netG=args.netG,
norm='instance',
drop_prob=args.drop_prob)
model = model.to(args.device)
return model
def load_model(checkpoint_file, netG):
checkpoint = torch.load(checkpoint_file)
args = checkpoint['args']
model = define_Gen(input_nc=1,
output_nc=1,
ngf=args.num_chans,
netG=netG,
norm='instance',
drop_prob=0.0)
model = model.to(args.device)
if args.data_parallel:
model = torch.nn.DataParallel(model)
model.load_state_dict(checkpoint['model'])
return model