opt.py

import argparse
import json

def get_opts():
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', type=str, required=True, help="config file for runing")
    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', 'llff_nocs', 'google_scanned', 'objectron', 'srn', 'srn_multi', 'objectron_multi', 'nocs_bckg', 'llff_nsff', 'co3d', 'pd', 'pd_multi_obj', 'pd_multi', 'pd_multi_ae', 'srn_multi_ae', 'pd_multi_obj_ae', 'pd_multi_obj_ae_nocs', 'pd_multi_obj_ae_cv', 'sapien', 'sapien_multi'],
                        help='which dataset to train/val')
    parser.add_argument('--output_path', type=str, default='./results', help='dir to save the training results.')
    parser.add_argument('--save_path', type=str,
                        default='vanilla',
                        help='save results during eval')
    parser.add_argument('--img_wh', nargs="+", type=int, default=[640, 480],
                        help='resolution (img_w, img_h) of the image')
    parser.add_argument('--white_back', default=False, action="store_true",
                        help='try for synthetic scenes like blender')
    parser.add_argument('--spheric_poses', default=True, action="store_true",
                        help='whether images are taken in spheric poses (for llff)')
    parser.add_argument('--emb_dim', type=int, default=2458,
                        help='Total number of different objects in a category')
    parser.add_argument('--latent_dim', type=int, default=256,
                        help='dim of latent each for shape and appearance')
    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=64,
                        help='number of coarse samples')
    parser.add_argument('--N_importance', type=int, default=64,
                        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('--crop_img', default=False, action="store_true",
                        help='initially crop the image or not')
    parser.add_argument('--use_image_encoder', default=False, action="store_true",
                        help='initially crop the image or not')
    parser.add_argument('--latent_code_path', type=str, default=None,
                        help='which category to use')
    parser.add_argument('--encoder_type', type=str, default='resnet',
                        help='which category to use')
    parser.add_argument('--finetune_lpips', default=False, action="store_true",
                        help='whether to finetune with lpips loss and patched dataloader')

    # params for SRN multicat training

    parser.add_argument('--splits', type=str, default=None,
                        help='which category to use')
                        
    parser.add_argument('--run_eval', default=False, action="store_true")

    parser.add_argument('--do_generate', default=False, action="store_true")
    parser.add_argument('--val_splits', type=str, default=None,
                        help='which category to use')
    parser.add_argument('--cat', type=str, default=None,
                        help='which category to use')
    parser.add_argument('--use_tcnn', default=False, action="store_true")

    parser.add_argument('--model_type', type=str, default='geometry',
                        help='which model to use i.e. geometry or render for refnerf')
    parser.add_argument('--train_opacity_rgb', default=False, action="store_true",
                        help='whether to train both opacity and rgb for voxel model')
                

    # params for latent codes:
    # 
    parser.add_argument('--N_max_objs', type=int, default=151,
                        help='maximum number of object instances in the dataset')
    
    #onl for nerfmvs
    parser.add_argument('--nv', type=int, default=3,
                        help='maximum number of object instances in the dataset')
    
    parser.add_argument('--num_nocs_ch', type=int, default=256,
                        help='maximum number of object instances in the dataset')
    parser.add_argument('--N_obj_code_length', type=int, default=128,
                        help='size of latent vector')
    ## params for Nerf Model 
    #(Scene branch)
    parser.add_argument('--D', type=int, default=8)
    parser.add_argument('--W', type=int, default=256)
    parser.add_argument('--N_freq_xyz', type=int, default=10)
    parser.add_argument('--N_freq_dir', type=int, default=4)
    parser.add_argument('--skips', type=list, default=[4])

    ## params for Nerf Model 
    #(Obj branch)
    parser.add_argument('--inst_D', type=int, default=4)
    parser.add_argument('--inst_W', type=int, default=128)
    parser.add_argument('--inst_skips', type=list, default=[2])
    parser.add_argument('--batch_size', type=int, default=1024,
                        help='batch size')
    # parser.add_argument('--chunk', type=int, default= 16*64,
    #                     help='chunk size to split the input to avoid OOM')
    parser.add_argument('--chunk', type=int, default= 16*240,
                        help='chunk size to split the input to avoid OOM')
    # 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=1,
                        help='number of gpus')

    parser.add_argument('--run_max_steps', type=int, default=100000,
                        help='number of gpus')

    parser.add_argument('--ckpt_path', type=str, default=None,
                        help='pretrained checkpoint to load (including optimizers, etc)')
    parser.add_argument('--is_optimize',  type=str, default=None,
                        help='whether to finetune the network after training on prior data')
    parser.add_argument('--prefix', 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)')

    
    #### Loss params
    parser.add_argument('--color_loss_weight', type=float, default=1.0)
    parser.add_argument('--depth_loss_weight', type=float, default=0.1)
    parser.add_argument('--opacity_loss_weight', type=float, default=10.0)
    parser.add_argument('--instance_color_loss_weight', type=float, default=1.0)
    parser.add_argument('--instance_depth_loss_weight', type=float, default=1.0)

    #### object-nerf optimizer params
    parser.add_argument('--optimizer', type=str, default='adam',
                        help='optimizer type',
                        choices=['sgd', 'adam', 'radam', 'ranger'])
    # parser.add_argument('--lr', type=float, default=1.0e-3,
    #                     help='learning rate')
    parser.add_argument('--lr', type=float, default=1.0e-3,
                        help='learning rate')
    parser.add_argument('--iters', type=int, default=30000,
                        help='iters')
    # parser.add_argument('--lr', type=float, default=1.0e-4,
    #                     help='learning rate')
    parser.add_argument('--latent_lr', type=float, default=1.0e-3,
                        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='poly',
                        help='scheduler type',
                        choices=['steplr', 'cosine', 'poly'])
    parser.add_argument('--lr_scheduler_latent', type=str, default='poly',
                        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')

    #### nerf_pl configs
    # 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=0.99,
                        help='exponent for polynomial learning rate decay')
    # parser.add_argument('--poly_exp', type=float, default=2,
    #                     help='exponent for polynomial learning rate decay')
    ###########################

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

    parser.add_argument('--render_name', type=str, default=None,
                        help='render directory name')  

    parser.add_argument('--exp_type', type=str, default='vanilla',
                        help='experiment type --choose from vanilla, pixel_nerf, pixel_nerf_sphere, groundplanar, triplanar')

    ###########################

    # parser.add_argument('--ckpt_path', type=str, default='last.ckpt',
    #                     help='ckpt path')

    args = parser.parse_args()
    # Load and parse the JSON configuration file
    with open(args.config, "r") as config_file:
        config_data = json.load(config_file)
        
    # required_args = ["urdf_file", "output_dir"]
    # missing_args = [arg for arg in required_args if arg not in config_data]
    # if missing_args:
    #     raise ValueError(f"Required argument(s) {', '.join(missing_args)} not found in the JSON configuration")

    # Update the args namespace with loaded JSON data
    for key, value in config_data.items():
        setattr(args, key, value)
        
    return args