ar_hops default val 1

main_sphere_compression.py

@@ -422,7 +422,7 @@ parser.add_argument('--nonlinearity', '-nl', default='GDN', type=str, choices=['
 parser.add_argument('--context-model', '-cm', default='', type=str, choices=['', 'checkerboard', 'autoregressive', 'full'], help="Context model to use. Empty string is no context model. 'full' is not causal (no masked convolution) and thus just for reference.")
 parser.add_argument('--autoregressive-relu', '-arrelu', action='store_true', help='use ReLU function after autoregressive convolution in context model. Default is no ReLU.')
 parser.add_argument('--autoregressive-abs', '-arabs', action='store_true', help='use absolute input values for autoregressive convolution in context model. Only recommended for scale prediction. Default is False.')
-parser.add_argument('--autoregressive-hops', '-arhops', type=int, default=2, help='Number of hops for neighborhood for autoregressive convolution in context model. Default is 2.')
+parser.add_argument('--autoregressive-hops', '-arhops', type=int, default=1, help='Number of hops for neighborhood for autoregressive convolution in context model. Default is 1.')
 parser.add_argument('--ar-no-single-conv', '-arnsc',  action='store_true', help="Use sequential convolutions for autoregressive convolution with hops > 1. Not recommended since theoretical and actual rates will differ.")
 parser.add_argument('--quality', '-q', default=4, type=int, choices=range(1, 9), help='Quality levels (1: lowest, highest: 8)')
 parser.add_argument('--lambda', dest='lmbda', type=float, default=1e-2, help='Bit-rate distortion parameter (default: %(default)s)')