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opt.py
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import configargparse
def config_parser(cmd=None):
parser = configargparse.ArgumentParser()
parser.add_argument('--config', is_config_file=True,
help='config file path')
parser.add_argument("--expname", type=str,
help='experiment name')
parser.add_argument("--basedir", type=str, default='./log',
help='where to store ckpts and logs')
parser.add_argument("--add_timestamp", type=int, default=0,
help='add timestamp to dir')
parser.add_argument("--datadir", type=str, default='./data/llff/fern',
help='input data directory')
parser.add_argument("--feature_dir", type=str, default='/mnt/sda/Projects/LLFF/dataset/desk/',
help='input feature directory')
parser.add_argument("--progress_refresh_rate", type=int, default=10,
help='how many iterations to show psnrs or iters')
parser.add_argument('--with_depth', action='store_true')
parser.add_argument('--ray_downsample_train', type=float, default=1.0)
parser.add_argument('--patch_downsample_train', type=float, default=1.0)
parser.add_argument('--downsample_test', type=float, default=1.0)
parser.add_argument('--model_name', type=str, default='TensorVMSplit',
choices=['TensorVMSplit', 'TensorCP'])
# loader options
parser.add_argument("--batch_size", type=int, default=4096)
parser.add_argument("--n_iters", type=int, default=30000)
parser.add_argument("--joint_start_iter", type=int, default=5000)
parser.add_argument('--dataset_name', type=str, default='blender',
choices=['blender', 'llff', 'nsvf', 'dtu','tankstemple', 'own_data'])
parser.add_argument("--patch_num", type=int, default=8,
help='patch num in every iteration')
# training options
parser.add_argument("--clip_input", type=float, default=1.,
help='percentage of clip input')
parser.add_argument("--test_prompt", default=None,
help='test the classes text prompt in which image')
parser.add_argument("--has_segmentation_maps", type=int, default=0,
help='if the dataset has segmentation maps')
parser.add_argument("--patch_size", type=int, default=256,
help='patch_size for training')
parser.add_argument("--chunk_size", type=int, default=4096,
help='chunk_size for training')
parser.add_argument("--temperature", type=float, default=1.,
help='temperature for softmax')
# learning rate
parser.add_argument("--lr_init", type=float, default=0.02,
help='learning rate')
parser.add_argument("--lr_basis", type=float, default=1e-4,
help='learning rate')
parser.add_argument("--lr_decay_iters", type=int, default=-1,
help = 'number of iterations the lr will decay to the target ratio; -1 will set it to n_iters')
parser.add_argument("--lr_decay_target_ratio", type=float, default=0.1,
help='the target decay ratio; after decay_iters inital lr decays to lr*ratio')
parser.add_argument("--lr_upsample_reset", type=int, default=1,
help='reset lr to inital after upsampling')
# loss
parser.add_argument("--L1_weight_inital", type=float, default=0.0,
help='loss weight')
parser.add_argument("--L1_weight_rest", type=float, default=0,
help='loss weight')
parser.add_argument("--Ortho_weight", type=float, default=0.0,
help='loss weight')
parser.add_argument("--TV_weight_density", type=float, default=0.0,
help='loss weight')
parser.add_argument("--TV_weight_app", type=float, default=0.0,
help='loss weight')
# correlation loss setting
parser.add_argument("--dino_shift", type=float, default=0.)
parser.add_argument("--dino_pos_weight", type=float, default=0.)
parser.add_argument("--dino_neg_weight", type=float, default=0.)
parser.add_argument("--relevancy_weight", type=float, default=0.)
# model
# volume options
parser.add_argument("--n_lamb_sigma", type=int, action="append")
parser.add_argument("--n_lamb_sh", type=int, action="append")
parser.add_argument("--data_dim_color", type=int, default=27)
parser.add_argument("--rm_weight_mask_thre", type=float, default=0.0001,
help='mask points in ray marching')
parser.add_argument("--alpha_mask_thre", type=float, default=0.0001,
help='threshold for creating alpha mask volume')
parser.add_argument("--distance_scale", type=float, default=25,
help='scaling sampling distance for computation')
parser.add_argument("--density_shift", type=float, default=-10,
help='shift density in softplus; making density = 0 when feature == 0')
# network decoder
parser.add_argument("--shadingMode", type=str, default="MLP_Fea",
help='which shading mode to use')
parser.add_argument("--pos_pe", type=int, default=6,
help='number of pe for pos')
parser.add_argument("--view_pe", type=int, default=0,
help='number of pe for view')
parser.add_argument("--fea_pe", type=int, default=0,
help='number of pe for features')
parser.add_argument("--featureC", type=int, default=128,
help='hidden feature channel in MLP')
# test option
parser.add_argument("--ckpt", type=str, default=None,
help='specific weights npy file to reload for coarse network')
parser.add_argument("--render_only", type=int, default=0)
parser.add_argument("--render_test", type=int, default=0)
parser.add_argument("--render_train", type=int, default=0)
parser.add_argument("--render_path", type=int, default=0)
parser.add_argument("--render_select", type=int, default=0)
parser.add_argument("--render_seg_train", type=int, default=0)
parser.add_argument("--render_seg_test", type=int, default=0)
parser.add_argument("--render_seg_path", type=int, default=0)
parser.add_argument("--render_seg_depth", type=int, default=0)
parser.add_argument("--render_feature", type=int, default=0)
parser.add_argument("--export_mesh", type=int, default=0)
parser.add_argument("--reference_text", type=str, default=None)
# rendering options
parser.add_argument('--lindisp', default=False, action="store_true",
help='use disparity depth sampling')
parser.add_argument("--perturb", type=float, default=1.,
help='set to 0. for no jitter, 1. for jitter')
parser.add_argument("--accumulate_decay", type=float, default=0.998)
parser.add_argument("--fea2denseAct", type=str, default='softplus')
parser.add_argument('--ndc_ray', type=int, default=0)
parser.add_argument('--nSamples', type=int, default=1e6,
help='sample point each ray, pass 1e6 if automatic adjust')
parser.add_argument('--step_ratio',type=float,default=0.5)
## blender flags
parser.add_argument("--white_bkgd", action='store_true',
help='set to render synthetic data on a white bkgd (always use for dvoxels)')
parser.add_argument('--N_voxel_init',
type=int,
default=100**3)
parser.add_argument('--N_voxel_final',
type=int,
default=300**3)
parser.add_argument("--upsamp_list", type=int, action="append")
parser.add_argument("--update_AlphaMask_list", type=int, action="append")
parser.add_argument('--idx_view',
type=int,
default=0)
# logging/saving options
parser.add_argument("--N_vis", type=int, default=5,
help='N images to vis')
parser.add_argument("--vis_every", type=int, default=10000,
help='frequency of visualize the image')
if cmd is not None:
return parser.parse_args(cmd)
else:
return parser.parse_args()