opt.py

import argparse

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

    parser.add_argument('--root_dir', type=str, required=True,
                        help='root directory of dataset')
    parser.add_argument('--dataset_name', type=str, default='blender',
                        choices=['blender', 'phototourism'],
                        help='which dataset to train/val')
    # for blender
    parser.add_argument('--data_perturb', nargs="+", type=str, default=[],
                        help='''what perturbation to add to data.
                                Available choices: [], ["color"], ["occ"] or ["color", "occ"]
                             ''')
    parser.add_argument('--img_wh', nargs="+", type=int, default=[800, 800],
                        help='resolution (img_w, img_h) of the image')
    # for phototourism
    parser.add_argument('--img_downscale', type=int, default=1,
                        help='how much to downscale the images for phototourism dataset')
    parser.add_argument('--use_cache', default=False, action="store_true",
                        help='whether to use ray cache (make sure img_downscale is the same)')

    # original NeRF parameters
    parser.add_argument('--N_emb_xyz', type=int, default=10,
                        help='number of xyz embedding frequencies')
    parser.add_argument('--N_emb_dir', type=int, default=4,
                        help='number of direction embedding frequencies')
    parser.add_argument('--N_samples', type=int, default=64,
                        help='number of coarse samples')
    parser.add_argument('--N_importance', type=int, default=128,
                        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')

    # NeRF-W parameters
    parser.add_argument('--N_vocab', type=int, default=100,
                        help='''number of vocabulary (number of images) 
                                in the dataset for nn.Embedding''')
    parser.add_argument('--encode_a', default=False, action="store_true",
                        help='whether to encode appearance (NeRF-A)')
    parser.add_argument('--N_a', type=int, default=48,
                        help='number of embeddings for appearance')
    parser.add_argument('--encode_t', default=False, action="store_true",
                        help='whether to encode transient object (NeRF-U)')
    parser.add_argument('--N_tau', type=int, default=16,
                        help='number of embeddings for transient objects')
    parser.add_argument('--beta_min', type=float, default=0.1,
                        help='minimum color variance for each ray')

    # Bundle-Adjusting Neural Radiance Fields
    parser.add_argument('--refine_pose', default=False, action="store_true",
                        help='whether to refine input camera poses (BARF)')

    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 path to load')
    parser.add_argument('--prefixes_to_ignore', nargs='+', type=str, default=['loss'],
                        help='the prefixes to ignore in the checkpoint state dict')

    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')
    parser.add_argument('--save_path', type=str, default='./ckpts',
                        help='paths to save checkpoints to')
    parser.add_argument('--refresh_every', type=int, default=1,
                        help='print the progress bar every X steps')

    return parser.parse_args()