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,
