# # Copyright (C) 2023, Inria # GRAPHDECO research group, https://team.inria.fr/graphdeco # All rights reserved. # # This software is free for non-commercial, research and evaluation use # under the terms of the LICENSE.md file. # # For inquiries contact george.drettakis@inria.fr # from argparse import ArgumentParser, Namespace import sys import os class GroupParams: pass class ParamGroup: def __init__(self, parser: ArgumentParser, name : str, fill_none = False): group = parser.add_argument_group(name) for key, value in vars(self).items(): shorthand = False if key.startswith("_"): shorthand = True key = key[1:] t = type(value) value = value if not fill_none else None if shorthand: if t == bool: group.add_argument("--" + key, ("-" + key[0:1]), default=value, action="store_true") else: group.add_argument("--" + key, ("-" + key[0:1]), default=value, type=t) else: if t == bool: group.add_argument("--" + key, default=value, action="store_true") else: group.add_argument("--" + key, default=value, type=t) def extract(self, args): group = GroupParams() for arg in vars(args).items(): if arg[0] in vars(self) or ("_" + arg[0]) in vars(self): setattr(group, arg[0], arg[1]) return group class ModelParams(ParamGroup): def __init__(self, parser, sentinel=False): self.sh_degree = 3 self._source_path = "" self._model_path = "" self._images = "images" self._resolution = -1 self._white_background = True # Technicolor configs override this default. # self._white_background = False self.data_device = "cuda" self.eval = True self.render_process=False self.add_points=False self.extension=".png" self.llffhold=8 self.technicolor_duration = 50 super().__init__(parser, "Loading Parameters", sentinel) def extract(self, args): g = super().extract(args) g.source_path = os.path.abspath(g.source_path) return g class PipelineParams(ParamGroup): def __init__(self, parser): self.convert_SHs_python = False self.compute_cov3D_python = False self.debug = False super().__init__(parser, "Pipeline Parameters") class ModelHiddenParams(ParamGroup): def __init__(self, parser): self.net_width = 128 # width of deformation MLP, larger will increase the rendering quality and decrase the training/rendering speed. self.timebase_pe = 4 self.defor_depth = 1 # depth of deformation MLP, larger will increase the rendering quality and decrase the training/rendering speed. self.posebase_pe = 10 self.scale_rotation_pe = 2 self.opacity_pe = 2 self.timenet_width = 64 self.timenet_output = 32 self.bounds = 1.6 self.plane_tv_weight = 0.0001 # TV loss of spatial grid self.time_smoothness_weight = 0.01 # TV loss of temporal grid self.l1_time_planes = 0.0001 # TV loss of temporal grid self.kplanes_config = { 'grid_dimensions': 2, 'input_coordinate_dim': 4, 'output_coordinate_dim': 32, 'resolution': [64, 64, 64, 25] # [64,64,64]: resolution of spatial grid. 25: resolution of temporal grid, better to be half length of dynamic frames } self.multires = [1, 2, 4, 8] # multi resolution of voxel grid self.no_dx=False # cancel the deformation of Gaussians' position self.no_grid=False # cancel the spatial-temporal hexplane. self.no_ds=False # cancel the deformation of Gaussians' scaling self.no_dr=False # cancel the deformation of Gaussians' rotations self.no_do=False # cancel the deformation of Gaussians' opacity self.no_dshs=False # cancel the deformation of SH colors. self.empty_voxel=False self.grid_pe=0 self.static_mlp=False self.apply_rotation=False self.h2m_voxel_conf = { 'resolutions': [(32,32,32), (64,64,64), (128,128,128)], 'emb_dim_per_level': 16, 'final_out_dim': 32, 'vertex_dhe_conf':{ 'num_hash_functions': 128, 'emb_dim': 16, 'prime': [200003, 1000003, 3000007], 'bucket_size': [20000, 120000, 320000], 'hidden_dim': 64, 'num_hidden_layers': 4 }, 'combine_mode': 'concat' } self.h2m_time_conf = { 'freq_L': 8, 'hidden': 32, 'out_dim': 32 } self.h2m_render_mode = False self.compute_smooth = False self.smooth_cfg = { 'spatial_perturb_range': 0.005, 'temporal_perturb_range': 0.005, 'lambda_spartial_tv': 0.5, 'lambda_temporal_tv': 0.5, 'downsample_ratio': 1.0, 'smooth_test_mode': 71 } super().__init__(parser, "ModelHiddenParams") class OptimizationParams(ParamGroup): def __init__(self, parser): self.dataloader=False self.zerostamp_init=False self.custom_sampler=None self.train_dynamic = True self.iterations = 30_000 self.coarse_iterations = 3000 self.position_lr_init = 0.00016 self.position_lr_final = 0.0000016 self.position_lr_delay_mult = 0.01 self.position_lr_max_steps = 20_000 self.deformation_lr_init = 0.00016 self.deformation_lr_final = 0.000016 self.deformation_lr_delay_mult = 0.01 self.grid_lr_init = 0.0002 self.grid_lr_final = 0.000002 self.feature_lr = 0.0025 self.opacity_lr = 0.05 self.opacity_head_lr_mult = 1.0 # optional multiplier for a dedicated opacity head self.scaling_lr = 0.005 self.rotation_lr = 0.001 self.percent_dense = 0.01 # self.lambda_dssim = 0.2 self.lambda_dssim = 0.0 self.lambda_lpips = 0 # Time smooth lambda # self.lambda_smooth = 0.5 self.lambda_smooth = 0.0 self.lambda_smooth_init = 0.0 self.lambda_smooth_final = 0.0 self.lambda_smooth_start_iter = 0 self.lambda_smooth_end_iter = 0 self.weight_constraint_init= 1 self.weight_constraint_after = 0.2 self.weight_decay_iteration = 5000 self.opacity_reset_interval = 3000 self.densification_interval = 100 self.densify_from_iter = 500 self.densify_until_iter = 20_000 self.densify_grad_threshold_coarse = 0.0002 self.densify_grad_threshold_fine_init = 0.0002 self.densify_grad_threshold_after = 0.0002 self.pruning_from_iter = 500 self.pruning_interval = 100 self.opacity_threshold_coarse = 0.005 self.opacity_threshold_fine_init = 0.005 self.opacity_threshold_fine_after = 0.005 self.batch_size=1 self.add_point=False self.n_neurons = 128 # may not use self.activation = "ReLU" self.dynamic_thro = 7.0 self.weight_prune_from_iter = 3000 self.weight_prune_interval = 3000 self.weight_prune_threshold = 0.02 super().__init__(parser, "Optimization Parameters") def get_combined_args(parser : ArgumentParser): cmdlne_string = sys.argv[1:] cfgfile_string = "Namespace()" args_cmdline = parser.parse_args(cmdlne_string) try: cfgfilepath = os.path.join(args_cmdline.model_path, "cfg_args") print("Looking for config file in", cfgfilepath) with open(cfgfilepath) as cfg_file: print("Config file found: {}".format(cfgfilepath)) cfgfile_string = cfg_file.read() except TypeError: print("Config file not found at") pass args_cfgfile = eval(cfgfile_string) merged_dict = vars(args_cfgfile).copy() for k,v in vars(args_cmdline).items(): if v != None: merged_dict[k] = v return Namespace(**merged_dict)