in fairnr/tasks/neural_rendering.py [0:0]
def add_args(parser):
"""Add task-specific arguments to the parser"""
parser.add_argument("data", help='data-path or data-directoy')
parser.add_argument("--object-id-path", type=str, help='path to object indices', default=None)
parser.add_argument("--no-preload", action="store_true")
parser.add_argument("--no-load-binary", action="store_true")
parser.add_argument("--load-depth", action="store_true",
help="load depth images if exists")
parser.add_argument("--transparent-background", type=str, default="1.0",
help="background color if the image is transparent")
parser.add_argument("--load-mask", action="store_true",
help="load pre-computed masks which is useful for subsampling during training.")
parser.add_argument("--train-views", type=str, default="0..50",
help="views sampled for training, you can set specific view id, or a range")
parser.add_argument("--valid-views", type=str, default="0..50",
help="views sampled for validation, you can set specific view id, or a range")
parser.add_argument("--test-views", type=str, default="0",
help="views sampled for rendering, only used for showing rendering results.")
parser.add_argument("--subsample-valid", type=int, default=-1,
help="if set > -1, subsample the validation (when training set is too large)")
parser.add_argument("--view-per-batch", type=int, default=6,
help="number of views training each batch (each GPU)")
parser.add_argument("--valid-view-per-batch", type=int, default=1,
help="number of views training each batch (each GPU)")
parser.add_argument("--view-resolution", type=str, default='64x64',
help="width for the squared image. downsampled from the original.")
parser.add_argument('--valid-view-resolution', type=str, default=None,
help="if not set, if valid view resolution will be train view resolution")
parser.add_argument("--min-color", choices=(0, -1), default=-1, type=int,
help="RGB range used in the model. conventionally used -1 ~ 1")
parser.add_argument("--virtual-epoch-steps", type=int, default=None,
help="virtual epoch used in Infinite Dataset. if None, set max-update")
parser.add_argument("--pruning-every-steps", type=int, default=None,
help="if the model supports pruning, prune unecessary voxels")
parser.add_argument("--half-voxel-size-at", type=str, default=None,
help='specific detailed number of updates to half the voxel sizes')
parser.add_argument("--reduce-step-size-at", type=str, default=None,
help='specific detailed number of updates to reduce the raymarching step sizes')
parser.add_argument("--prune-voxel-at", type=str, default=None,
help='specific detailed number of pruning voxels')
parser.add_argument("--rendering-every-steps", type=int, default=None,
help="if set, enables rendering online with default parameters")
parser.add_argument("--rendering-args", type=str, metavar='JSON')
parser.add_argument("--pruning-th", type=float, default=0.5,
help="if larger than this, we choose keep the voxel.")
parser.add_argument("--pruning-with-train-stats", action='store_true',
help="if set, model will run over the training set statstics to prune voxels.")
parser.add_argument("--pruning-rerun-train-set", action='store_true',
help="only works when --pruning-with-train-stats is also set.")
parser.add_argument("--output-valid", type=str, default=None)