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#
# 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
def load_yaml(self, opts=None):
if opts is None:
return
else:
for key, value in opts.items():
try:
setattr(self, key, value)
except:
raise Exception(f'Unknown attribute {key}')
class GuidanceParams(ParamGroup):
def __init__(self, parser, opts=None):
self.guidance = "SD"
self.g_device = "cuda"
self.model_key = None
self.is_safe_tensor = False
self.base_model_key = None
self.controlnet_model_key = None
self.perpneg = True
self.negative_w = -2.
self.front_decay_factor = 2.
self.side_decay_factor = 10.
self.vram_O = False
self.fp16 = True
self.hf_key = None
self.t_range = [0.02, 0.5]
self.max_t_range = 0.98
self.scheduler_type = 'DDIM'
self.num_train_timesteps = None
self.sds = False
self.fix_noise = False
self.noise_seed = 0
self.ddim_inv = False
self.delta_t = 80
self.delta_t_start = 100
self.annealing_intervals = True
self.text = ''
self.inverse_text = ''
self.textual_inversion_path = None
self.LoRA_path = None
self.controlnet_ratio = 0.5
self.negative = ""
self.guidance_scale = 7.5
self.denoise_guidance_scale = 1.0
self.lambda_guidance = 1.
self.xs_delta_t = 200
self.xs_inv_steps = 5
self.xs_eta = 0.0
# multi-batch
self.C_batch_size = 1
self.vis_interval = 100
super().__init__(parser, "Guidance Model Parameters")
class ModelParams(ParamGroup):
def __init__(self, parser, sentinel=False, opts=None):
self.sh_degree = 0
self._source_path = ""
self._model_path = ""
self.pretrained_model_path = None
self._images = "images"
self.workspace = "debug"
self.batch = 10
self._resolution = -1
self._white_background = True
self.data_device = "cuda"
self.eval = False
self.opt_path = None
# augmentation
self.sh_deg_aug_ratio = 0.1
self.bg_aug_ratio = 0.5
self.shs_aug_ratio = 0.0
self.scale_aug_ratio = 1.0
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, opts=None):
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, opts=None):
self.iterations = 5000# 10_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 = 30_000
self.feature_lr = 0.0050
self.feature_lr_final = 0.0030
self.opacity_lr = 0.05
self.scaling_lr = 0.005
self.rotation_lr = 0.001
self.geo_iter = 0
self.as_latent_ratio = 0.2
# dense
self.resnet_lr = 1e-4
self.resnet_lr_init = 2e-3
self.resnet_lr_final = 5e-5
self.scaling_lr_final = 0.001
self.rotation_lr_final = 0.0002
self.percent_dense = 0.003
self.densify_grad_threshold = 0.00075
self.lambda_tv = 1.0 # 0.1
self.lambda_bin = 10.0
self.lambda_scale = 1.0
self.lambda_sat = 1.0
self.lambda_radius = 1.0
self.densification_interval = 100
self.opacity_reset_interval = 300
self.densify_from_iter = 100
self.densify_until_iter = 30_00
self.use_control_net_iter = 10000000
self.warmup_iter = 1500
self.use_progressive = False
self.save_process = True
self.pro_frames_num = 600
self.pro_render_45 = False
self.progressive_view_iter = 500
self.progressive_view_init_ratio = 0.2
self.scale_up_cameras_iter = 500
self.scale_up_factor = 0.95
self.fovy_scale_up_factor = [0.75, 1.1]
self.phi_scale_up_factor = 1.5
super().__init__(parser, "Optimization Parameters")
class GenerateCamParams(ParamGroup):
def __init__(self, parser):
self.init_shape = 'sphere'
self.init_prompt = ''
self.use_pointe_rgb = False
self.radius_range = [5.2, 5.5] #[3.8, 4.5] #[3.0, 3.5]
self.max_radius_range = [3.5, 5.0]
self.default_radius = 3.5
self.theta_range = [45, 105]
self.max_theta_range = [45, 105]
self.phi_range = [-180, 180]
self.max_phi_range = [-180, 180]
self.fovy_range = [0.32, 0.60] #[0.3, 1.5] #[0.5, 0.8] #[10, 30]
self.max_fovy_range = [0.16, 0.60]
self.rand_cam_gamma = 1.0
self.angle_overhead = 30
self.angle_front =60
self.render_45 = True
self.uniform_sphere_rate = 0
self.image_w = 512
self.image_h = 512 # 512
self.SSAA = 1
self.init_num_pts = 100_000
self.default_polar = 90
self.default_azimuth = 0
self.default_fovy = 0.55 #20
self.jitter_pose = True
self.jitter_center = 0.05
self.jitter_target = 0.05
self.jitter_up = 0.01
self.device = "cuda"
super().__init__(parser, "Generate Cameras 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)
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