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opt.py
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opt.py
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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=['llff_ray_patch_1image_proj', 'blender_ray_patch_1image_proj',
'blender_ray_patch_1image_rot3d', 'dtu_proj'],
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('--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')
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=80,
help='number of training epochs')
parser.add_argument('--num_gpus', type=int, default=4,
help='number of gpus')
parser.add_argument('--ckpt_path', type=str, default=None,
help='pretrained checkpoint path to load')
# load everything
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')
###########################
# my extra params
parser.add_argument('--with_ref', default=False, action="store_true")
parser.add_argument('--patch_size', type=int, default=-1)
# for llff / dtu
parser.add_argument('--patch_size_x', type=int, default=-1)
parser.add_argument('--patch_size_y', type=int, default=-1)
parser.add_argument('--pt_model', type=str, default=None)
# load ckpt only
parser.add_argument('--model', type=str,
default="nerf", choices=['sinnerf'])
parser.add_argument('--repeat', type=int, default=1)
# change ray sampling sparsity when generating rays
parser.add_argument('--nW', type=int, default=32)
# change ray num per patch (not used)
parser.add_argument('--nH', type=int, default=32)
# change ray num per patch (not used)
parser.add_argument('--sW', type=int, default=1)
# change ray sampling stride
parser.add_argument('--sH', type=int, default=1)
# change ray sampling stride
parser.add_argument('--dloss', type=str, default="hinge")
# discriminator loss type
parser.add_argument('--load_depth', default=False,
action="store_true") # use depth
parser.add_argument('--nerf_only', default=False, action="store_true")
# load weight of nerf only from checkpoint
parser.add_argument('--depth_type', type=str,
default='nerf') # depth supervision type
# weight of discriminator loss
parser.add_argument('--dis_weight', type=float, default=0.001)
# weight of loss on projected views
parser.add_argument('--proj_weight', type=float, default=1)
parser.add_argument('--angle', type=int, default=20) # angle for rot3d
parser.add_argument('--scan', type=int, default=4) # for dtu dataset
# weight for depth supervision
parser.add_argument('--depth_weight', type=float, default=0.05)
parser.add_argument('--vit_weight', type=float,
default=0) # weight for vit loss
parser.add_argument('--depth_smooth_weight', type=float, default=0)
parser.add_argument('--depth_anneal', default=False, action="store_true")
parser.add_argument('--loss_type', type=str, default='mse',
choices=['mse', 'ft', 'clip', 'l2_ssim', 'l2_vgg'], help='loss to use')
parser.add_argument('--patch_loss', type=str, default='mse',
choices=['mse', 'ft', 'clip', 'l2_ssim', 'l2_vgg'], help='loss to use')
return parser.parse_args()