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config.py
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config.py
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import configargparse
def config_parser():
parser = configargparse.ArgumentParser()
# general
parser.add_argument("--config", is_config_file=True, help="config file path")
parser.add_argument(
"--rootdir",
type=str,
default="./",
help="the path to the project root directory. Replace this path with yours!",
)
parser.add_argument("--expname", type=str, help="experiment name")
parser.add_argument("--distributed", action="store_true", help="if use distributed training")
#mark here for modify the default gpu from 0 to 1
parser.add_argument("--local_rank", type=int, default=0, help="rank for distributed training")
parser.add_argument(
"-j",
"--workers",
default=8,
type=int,
metavar="N",
help="number of data loading workers (default: 8)",
)
########## dataset options ##########
## train and eval dataset
parser.add_argument(
"--train_dataset",
type=str,
default="ibrnet_collected",
help="the training dataset, should either be a single dataset, "
'or multiple datasets connected with "+", for example, ibrnet_collected+llff+spaces',
)
parser.add_argument(
"--dataset_weights",
nargs="+",
type=float,
default=[],
help="the weights for training datasets, valid when multiple datasets are used.",
)
parser.add_argument(
"--train_scenes",
nargs="+",
default=[],
help="optional, specify a subset of training scenes from training dataset",
)
parser.add_argument(
"--eval_dataset", type=str, default="llff_test", help="the dataset to evaluate"
)
parser.add_argument(
"--eval_scenes",
nargs="+",
default=[],
help="optional, specify a subset of scenes from eval_dataset to evaluate",
)
## others
parser.add_argument(
"--testskip",
type=int,
default=8,
help="will load 1/N images from test/val sets, "
"useful for large datasets like deepvoxels or nerf_synthetic",
)
########## model options ##########
## ray sampling options
parser.add_argument(
"--sample_mode",
type=str,
default="uniform",
help="how to sample pixels from images for training:" "uniform|center",
)
parser.add_argument(
"--center_ratio", type=float, default=0.8, help="the ratio of center crop to keep"
)
parser.add_argument(
"--N_rand",
type=int,
default=32 * 16,
help="batch size (number of random rays per gradient step)",
)
parser.add_argument(
"--chunk_size",
type=int,
default=1024 * 4,
help="number of rays processed in parallel, decrease if running out of memory",
)
## model options
parser.add_argument(
"--coarse_feat_dim", type=int, default=32, help="2D feature dimension for coarse level"
)
parser.add_argument(
"--fine_feat_dim", type=int, default=32, help="2D feature dimension for fine level"
)
#mark here for modifying nume_source_view
parser.add_argument(
"--num_source_views",
type=int,
default=10,
help="the number of input source views for each target view",
)
parser.add_argument(
"--rectify_inplane_rotation", action="store_true", help="if rectify inplane rotation"
)
parser.add_argument("--coarse_only", action="store_true", help="use coarse network only")
parser.add_argument(
"--anti_alias_pooling", type=int, default=1, help="if use anti-alias pooling"
)
parser.add_argument("--trans_depth", type=int, default=4, help="number of transformer layers")
parser.add_argument("--netwidth", type=int, default=64, help="network intermediate dimension")
parser.add_argument(
"--single_net",
type=bool,
default=True,
help="use single network for both coarse and/or fine sampling",
)
########## checkpoints ##########
parser.add_argument(
"--no_reload", action="store_true", help="do not reload weights from saved ckpt"
)
parser.add_argument(
"--ckpt_path",
type=str,
default="",
help="specific weights npy file to reload for coarse network",
)
parser.add_argument(
"--no_load_opt", action="store_true", help="do not load optimizer when reloading"
)
parser.add_argument(
"--no_load_scheduler", action="store_true", help="do not load scheduler when reloading"
)
########### iterations & learning rate options ##########
parser.add_argument("--n_iters", type=int, default=400000, help="num of iterations")
parser.add_argument(
"--lrate_feature", type=float, default=1e-3, help="learning rate for feature extractor"
)
parser.add_argument("--lrate_gnt", type=float, default=5e-4, help="learning rate for gnt")
parser.add_argument(
"--lrate_decay_factor",
type=float,
default=0.5,
help="decay learning rate by a factor every specified number of steps",
)
parser.add_argument(
"--lrate_decay_steps",
type=int,
default=50000,
help="decay learning rate by a factor every specified number of steps",
)
########## rendering options ##########
parser.add_argument(
"--N_samples", type=int, default=64, help="number of coarse samples per ray"
)
parser.add_argument(
"--N_importance", type=int, default=64, help="number of important samples per ray"
)
parser.add_argument(
"--inv_uniform", action="store_true", help="if True, will uniformly sample inverse depths"
)
parser.add_argument(
"--det", action="store_true", help="deterministic sampling for coarse and fine samples"
)
parser.add_argument(
"--white_bkgd",
action="store_true",
help="apply the trick to avoid fitting to white background",
)
parser.add_argument(
"--render_stride",
type=int,
default=1,
help="render with large stride for validation to save time",
)
########## logging/saving options ##########
parser.add_argument("--i_print", type=int, default=100, help="frequency of terminal printout")
parser.add_argument(
"--i_img", type=int, default=500, help="frequency of tensorboard image logging"
)
parser.add_argument(
"--i_weights", type=int, default=10000, help="frequency of weight ckpt saving"
)
########## evaluation options ##########
parser.add_argument(
"--llffhold",
type=int,
default=8,
help="will take every 1/N images as LLFF test set, paper uses 8",
)
### mark here for adding parser
parser.add_argument(
"--few_shot_input",
type=bool,
default=False,
help="set true if only few-shot input"
)
return parser