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opt.py
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opt.py
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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