cloud_nerf/opt.py at main · KAIST-VICLab/cloud_nerf · GitHub

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import argparse


def get_opts():
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--root_dir",
        type=str,
        default="/home/ubuntu/data/nerf_example_data/nerf_synthetic/lego",
        help="root directory of dataset",
    )
    parser.add_argument(
        "--dataset_name", type=str, default="blender", choices=["blender", "llff"], help="which dataset to train/val"
    )
    parser.add_argument(
        "--img_wh", nargs="+", type=int, default=[800, 800], help="resolution (img_w, img_h) of the image"
    )
    parser.add_argument(
        "--spheric_poses",
        default=False,
        action="store_true",
        help="whether images are taken in spheric poses (for llff)",
    )

    parser.add_argument(
        "--not_use_mvs",
        default=False,
        action="store_true",
        help="whether exclude the mvs points cloud",
    )

    parser.add_argument(
        "--use_sh_feat",
        default=False,
        action="store_true",
        help="whether use spherical harmonics features",
    )

    parser.add_argument("--N_emb_xyz", type=int, default=10,
                        help="number of frequencies in xyz positional encoding")
    parser.add_argument("--N_emb_dir", type=int, default=4,
                        help="number of frequencies in dir positional encoding")
    parser.add_argument("--N_samples", type=int, default=128,
                        help="number of coarse samples")
    parser.add_argument("--N_importance", type=int, default=0,
                        help="number of additional fine samples")
    parser.add_argument("--use_disp", default=False,
                        action="store_true", help="use disparity depth sampling")
    parser.add_argument("--perturb", type=float, default=1.0,
                        help="factor to perturb depth sampling points")
    parser.add_argument("--noise_std", type=float, default=1.0,
                        help="std dev of noise added to regularize sigma")

    parser.add_argument("--batch_size", type=int,
                        default=1024, help="batch size")
    parser.add_argument("--chunk", type=int, default=32 *
                        1024, help="chunk size to split the input to avoid OOM")
    parser.add_argument("--num_epochs", type=int, default=16,
                        help="number of training epochs")
    parser.add_argument("--num_gpus", type=int,
                        default=1, help="number of gpus")

    parser.add_argument(
        "--ckpt_path", type=str, default=None, help="pretrained checkpoint to load (including optimizers, etc)"
    )
    parser.add_argument(
        "--prefixes_to_ignore",
        nargs="+",
        type=str,
        default=["loss"],
        help="the prefixes to ignore in the checkpoint state dict",
    )
    parser.add_argument(
        "--weight_path", type=str, default=None, help="pretrained model weight to load (do not load optimizers, etc)"
    )

    parser.add_argument(
        "--optimizer", type=str, default="adam", help="optimizer type", choices=["sgd", "adam", "radam", "ranger"]
    )
    parser.add_argument("--lr", type=float, default=5e-4, help="learning rate")
    parser.add_argument("--momentum", type=float,
                        default=0.9, help="learning rate momentum")
    parser.add_argument("--weight_decay", type=float,
                        default=0, help="weight decay")
    parser.add_argument(
        "--lr_scheduler", type=str, default="steplr", help="scheduler type", choices=["steplr", "cosine", "poly"]
    )
    # params for warmup, only applied when optimizer == 'sgd' or 'adam'
    parser.add_argument(
        "--warmup_multiplier", type=float, default=1.0, help="lr is multiplied by this factor after --warmup_epochs"
    )
    parser.add_argument(
        "--warmup_epochs", type=int, default=0, help="Gradually warm-up(increasing) learning rate in optimizer"
    )
    ###########################
    # params for steplr
    parser.add_argument("--decay_step", nargs="+", type=int,
                        default=[20], help="scheduler decay step")
    parser.add_argument("--decay_gamma", type=float,
                        default=0.1, help="learning rate decay amount")
    ###########################
    # params for poly
    parser.add_argument("--poly_exp", type=float, default=0.9,
                        help="exponent for polynomial learning rate decay")
    ###########################

    parser.add_argument("--exp_name", type=str,
                        default="exp", help="experiment name")

    return parser.parse_args()