Method modify_commandline_options

models/fastcut_model.py:19–48

Configures options specific for FASTCUT model

(parser, is_train=True)

    """
    @staticmethod
    def modify_commandline_options(parser, is_train=True):
        """  Configures options specific for FASTCUT model
        """
        parser.add_argument('--lambda_GAN', type=float, default=1.0, help='weight for GAN loss：GAN(G(X))')
        parser.add_argument('--lambda_NCE', type=float, default=1.0, help='weight for NCE loss: NCE(G(X), X)')
        parser.add_argument('--lambda_IDT', type=float, default=10.0, help='weight for NCE loss: NCE(G(X), X)')
        parser.add_argument('--nce_idt', type=util.str2bool, nargs='?', const=True, default=False, help='use NCE loss for identity mapping: NCE(G(Y), Y))')
        parser.add_argument('--nce_layers', type=str, default='0,4,8,12,16', help='compute NCE loss on which layers')
        parser.add_argument('--nce_includes_all_negatives_from_minibatch',
                            type=util.str2bool, nargs='?', const=True, default=False,
                            help='(used for single image translation) If True, include the negatives from the other samples of the minibatch when computing the contrastive loss. Please see models/patchnce.py for more details.')
        parser.add_argument('--netF', type=str, default='mlp_sample', choices=['sample', 'reshape', 'mlp_sample'], help='how to downsample the feature map')
        parser.add_argument('--netF_nc', type=int, default=256)
        parser.add_argument('--nce_T', type=float, default=0.07, help='temperature for NCE loss')
        parser.add_argument('--num_patches', type=int, default=256, help='number of patches per layer')
        parser.add_argument('--flip_equivariance',
                            type=util.str2bool, nargs='?', const=True, default=False,
                            help="Enforce flip-equivariance as additional regularization. It's used by FastCUT, but not CUT")

        parser.set_defaults(pool_size=0)  # no image pooling

        opt, _ = parser.parse_known_args()

        # Set default parameters for CUT and FastCUT
        parser.set_defaults(
            nce_idt=False, lambda_NCE=10.0, flip_equivariance=True,
            n_epochs=150, n_epochs_decay=50
            )

        return parser

    def __init__(self, opt):
        BaseModel.__init__(self, opt)