BAGS/arguments/__init__.py

#
# 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:
                    if value is False:
                        group.add_argument("--" + key, default=value, action="store_true")
                    else:
                        group.add_argument("--" + key, default=value, action="store_false")
                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 = False
        self.data_device = "cuda"
        self.eval = False
        self._kernel_size = 0.3   # v2: anti-aliasing (was 0.1)
        # self.use_spatial_gaussian_bias = False
        self.ray_jitter = False
        self.resample_gt_image = False
        self.load_allres = False
        self.sample_more_highres = False
        self.llffhold = 8
        print('$$',self._resolution)
        self.load_strict = False
        self.kernel_size1 = 5
        self.kernel_size2 = 9
        self.kernel_size3 = 21   # v2: larger blur kernel (17→21, 33 OOMs on 1296×968)
        self.kernel_size_ss = 21  # v2: larger blur kernel
        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 OptimizationParams(ParamGroup):
    def __init__(self, parser):
        self.iterations = 60_000
        self.position_lr_init = 0.00016
        self.position_lr_final = 0.0000016
        self.position_lr_delay_mult = 0.01
        self.position_lr_max_steps = 60_000
        self.feature_lr = 0.0025
        self.opacity_lr = 0.05
        self.scaling_lr = 0.005
        self.rotation_lr = 0.001
        self.percent_dense = 0.01
        self.lambda_dssim = 0.2
        self.densification_interval = 100
        self.opacity_reset_interval = 3000
        self.densify_from_iter = 500
        self.densify_until_iter = 35_000
        self.ms_steps = 6000
        self.not_use_rgbd = False
        self.not_use_pe = False
        self.init_dgt = 0.0006
        self.densify_grad_threshold = 0.0002
        self.init_opacity = -1.0
        self.min_opacity = 0.005
        self.no_bpn = False          # skip BPN entirely → vanilla MipSplatting
        self.use_depth_loss = False  # controlled via --use_depth_loss flag (store_true)
        self.depth_loss_alpha = 0.01  # v2: tuned alpha
        self.use_mask_loss = True
        self.mask_loss_alpha = 0.001
        self.use_rgbtv_loss = False
        self.rgbtv_loss_alpha = 0.001
        self.use_another_mlp = False
        # BPN's blur-synthesis target (mask*rgb + (1-mask)*render vs RAW input):
        # RAW frame cache (load_raw_blurry_cache) + sharp-frame skip set.
        self.raw_blurry_glob = ""
        self.raw_blurry_stride = 10
        self.bpn_skip_sharp_json = ""
        # BPN MLP (mlp_rgb_ss/mlp_rgb_ms) param-group lr split by name:
        # 'head' params (kernel) -> bpn_lr_kernel, 'mask' params -> bpn_lr_mask,
        # everything else (backbone) -> bpn_lr_kernel. Defaults match BAGS'
        # native KERLR=5e-4 * lr_scale(ks3=21)~=0.8997 -> ~4.5e-4 for both.
        self.bpn_lr_kernel = 4.5e-4
        self.bpn_lr_mask = 4.5e-4
        # Global cap on total gaussian count (mirrors TriSplat's --max_shapes).
        # 0 = disabled (no cap, original BAGS behavior).
        self.max_shapes = 0
        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)