help='multiply by a gamma every lr_decay_iters iterations')
parser.add_argument('--clip_grad_norm',
type=int,
default=0,
help='performs gradient clipping')
parser.add_argument(
'--gamma',
type=float,
default=0.5,
help='factor to decay learning rate every lr_decay_iters with')
# Loss settings
parser.add_argument('--loss_criterion',
type=str,
default='mse',
choices=allowed_losses(),
help="loss criterion")
parser.add_argument('--print_freq',
type=int,
default=10000,
help="print every x iterations")
parser.add_argument('--save_freq',
type=int,
default=100000,
help="save every x interations")
parser.add_argument('--metric',
type=str,
default='rmse',
choices=allowed_metrics(),
help="metric to use during evaluation")
parser.add_argument('--metric_1',
# parser.add_argument('--data_path', default='/esat/pyrite/wvangans/Datasets/KITTI/Depth_Completion/data/', help='path to desired dataset')
# Optimizer settings
parser.add_argument('--optimizer', type=str, default='adam', help='adam or sgd')
parser.add_argument('--weight_init', type=str, default='kaiming', help='normal, xavier, kaiming, orhtogonal weights initialisation')
parser.add_argument('--weight_decay', type=float, default=0, help='L2 weight decay/regularisation on?')
parser.add_argument('--lr_decay', action='store_true', help='decay learning rate with rule')
parser.add_argument('--niter', type=int, default=50, help='# of iter at starting learning rate')
parser.add_argument('--niter_decay', type=int, default=400, help='# of iter to linearly decay learning rate to zero')
parser.add_argument('--lr_policy', type=str, default=None, help='{}learning rate policy: lambda|step|plateau')
parser.add_argument('--lr_decay_iters', type=int, default=7, help='multiply by a gamma every lr_decay_iters iterations')
parser.add_argument('--clip_grad_norm', type=int, default=0, help='performs gradient clipping')
parser.add_argument('--gamma', type=float, default=0.5, help='factor to decay learning rate every lr_decay_iters with')
# Loss settings
parser.add_argument('--loss_criterion', type=str, default='mse', choices=allowed_losses(), help="loss criterion")
parser.add_argument('--print_freq', type=int, default=10000, help="print every x iterations")
parser.add_argument('--save_freq', type=int, default=100000, help="save every x interations")
parser.add_argument('--metric', type=str, default='rmse', choices=allowed_metrics(), help="metric to use during evaluation")
parser.add_argument('--metric_1', type=str, default='mae', choices=allowed_metrics(), help="metric to use during evaluation")
parser.add_argument('--wlid', type=float, default=0.1, help="weight base loss")
parser.add_argument('--wrgb', type=float, default=0.1, help="weight base loss")
parser.add_argument('--wpred', type=float, default=1, help="weight base loss")
parser.add_argument('--wguide', type=float, default=0.1, help="weight base loss")
# Cudnn
parser.add_argument("--cudnn", type=str2bool, nargs='?', const=True,
default=True, help="cudnn optimization active")
parser.add_argument('--gpu_ids', default='1', type=str, help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument("--mask_intersec", type=str2bool, nargs='?', const=True,
default=False, help="use mask_gt - mask_input as final mask for loss calculation")
parser.add_argument('--num_bins', type=int, default=1, help="Number of bins for weight map")