mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-24 21:13:46 +00:00
989320fdf2
* Provide --data_on_cpu option to save VRAM for training when there are many training images such as in large scene, most of the VRAM are used to store training data, use --data_on_cpu can help reduce VRAM and make it possible to train on GPU with less VRAM * Fix data_on_cpu effect on default mask * --data_on_cpu to --data_device * update readme * format warning infos
111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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from argparse import ArgumentParser, Namespace
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import sys
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import os
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class GroupParams:
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pass
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class ParamGroup:
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def __init__(self, parser: ArgumentParser, name : str, fill_none = False):
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group = parser.add_argument_group(name)
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for key, value in vars(self).items():
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shorthand = False
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if key.startswith("_"):
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shorthand = True
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key = key[1:]
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t = type(value)
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value = value if not fill_none else None
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if shorthand:
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if t == bool:
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group.add_argument("--" + key, ("-" + key[0:1]), default=value, action="store_true")
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else:
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group.add_argument("--" + key, ("-" + key[0:1]), default=value, type=t)
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else:
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if t == bool:
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group.add_argument("--" + key, default=value, action="store_true")
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else:
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group.add_argument("--" + key, default=value, type=t)
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def extract(self, args):
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group = GroupParams()
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for arg in vars(args).items():
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if arg[0] in vars(self) or ("_" + arg[0]) in vars(self):
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setattr(group, arg[0], arg[1])
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return group
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class ModelParams(ParamGroup):
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def __init__(self, parser, sentinel=False):
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self.sh_degree = 3
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self._source_path = ""
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self._model_path = ""
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self._images = "images"
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self._resolution = -1
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self._white_background = False
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self.data_device = "cuda"
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self.eval = False
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super().__init__(parser, "Loading Parameters", sentinel)
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def extract(self, args):
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g = super().extract(args)
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g.source_path = os.path.abspath(g.source_path)
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return g
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class PipelineParams(ParamGroup):
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def __init__(self, parser):
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self.convert_SHs_python = False
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self.compute_cov3D_python = False
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super().__init__(parser, "Pipeline Parameters")
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class OptimizationParams(ParamGroup):
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def __init__(self, parser):
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self.iterations = 30_000
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self.position_lr_init = 0.00016
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self.position_lr_final = 0.0000016
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self.position_lr_delay_mult = 0.01
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self.position_lr_max_steps = 30_000
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self.feature_lr = 0.0025
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self.opacity_lr = 0.05
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self.scaling_lr = 0.001
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self.rotation_lr = 0.001
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self.percent_dense = 0.01
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self.lambda_dssim = 0.2
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self.densification_interval = 100
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self.opacity_reset_interval = 3000
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self.densify_from_iter = 500
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self.densify_until_iter = 15_000
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self.densify_grad_threshold = 0.0002
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super().__init__(parser, "Optimization Parameters")
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def get_combined_args(parser : ArgumentParser):
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cmdlne_string = sys.argv[1:]
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cfgfile_string = "Namespace()"
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args_cmdline = parser.parse_args(cmdlne_string)
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try:
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cfgfilepath = os.path.join(args_cmdline.model_path, "cfg_args")
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print("Looking for config file in", cfgfilepath)
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with open(cfgfilepath) as cfg_file:
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print("Config file found: {}".format(cfgfilepath))
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cfgfile_string = cfg_file.read()
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except TypeError:
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print("Config file not found at")
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pass
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args_cfgfile = eval(cfgfile_string)
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merged_dict = vars(args_cfgfile).copy()
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for k,v in vars(args_cmdline).items():
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if v != None:
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merged_dict[k] = v
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return Namespace(**merged_dict)
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