[docs]
def add_args2parser_vae(parser):
parser.add_argument(
"--zd_dim", type=int, default=64, help="diva: size of latent space for domain"
)
parser.add_argument(
"--zx_dim",
type=int,
default=0,
help="diva: size of latent space for unobserved",
)
parser.add_argument(
"--zy_dim",
type=int,
default=64,
help="diva, hduva: size of latent space for class",
)
# HDUVA
parser.add_argument(
"--topic_dim", type=int, default=3, help="hduva: number of topics"
)
parser.add_argument(
"--nname_encoder_x2topic_h",
type=str,
default=None,
help="hduva: network from image to topic distribution",
)
parser.add_argument(
"--npath_encoder_x2topic_h",
type=str,
default=None,
help="hduva: network from image to topic distribution",
)
parser.add_argument(
"--nname_encoder_sandwich_x2h4zd",
type=str,
default=None,
help="hduva: network from image and topic to zd",
)
parser.add_argument(
"--npath_encoder_sandwich_x2h4zd",
type=str,
default=None,
help="hduva: network from image and topic to zd",
)
# ERM, ELBO
parser.add_argument(
"--gamma_y",
type=float,
default=None,
help="diva, hduva: multiplier for y classifier",
)
parser.add_argument(
"--gamma_d",
type=float,
default=None,
help="diva: multiplier for d classifier from zd",
)
# Beta VAE part
parser.add_argument(
"--beta_t", type=float, default=1.0, help="hduva: multiplier for KL topic"
)
parser.add_argument(
"--beta_d", type=float, default=1.0, help="diva: multiplier for KL d"
)
parser.add_argument(
"--beta_x", type=float, default=1.0, help="diva: multiplier for KL x"
)
parser.add_argument(
"--beta_y", type=float, default=1.0, help="diva, hduva: multiplier for KL y"
)
return parser