save_dir = f'./results/biggan_256/hybridng_{fn}' var_manager = VariableManager() loss_fn = LF.ProjectionLoss() # (4) define input output variable structure. the variable name must match # the argument name of the model and loss function call var_manager.register( variable_name='z', shape=(128, ), grad_free=True, distribution=dist.TruncatedNormalModulo(sigma=1.0, trunc=args.truncate), var_type='input', learning_rate=args.lr, hook_fn=hook.Clamp(args.truncate), ) var_manager.register( variable_name='c', shape=(128, ), requires_grad=True, default=model.get_class_embedding(class_lbl)[0], var_type='input', learning_rate=0.01, ) var_manager.register(variable_name='target', shape=(3, 256, 256), requires_grad=False, default=target,
var_manager = VariableManager() var_manager.register( variable_name='z', shape=(512,), default=None, grad_free=True, distribution=dist.TruncatedNormalModulo( sigma=1.0, trunc=args.truncate ), var_type='input', learning_rate=args.lr, hook_fn=hook.Compose( hook.NormalPerturb(sigma=args.latent_noise), hook.Clamp(trunc=args.truncate), ) ) var_manager.register( variable_name='target', shape=(3, 512, 512), requires_grad=False, default=target, var_type='output' ) var_manager.register( variable_name='weight', shape=(3, 512, 512), requires_grad=False,