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options.py
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options.py
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from custom_types import *
import os
import pickle
import json
import constants as const
import argparse
import sys
from functools import reduce
class Options:
@property
def name(self) -> str:
return f'{self.mesh_name}_{self.tag}'
@property
def cp_folder(self) -> str:
return f'{const.PROJECT_ROOT}/checkpoints/{self.name}'
@property
def save_path(self) -> str:
return f'{self.cp_folder}/options.pkl'
@property
def already_saved(self) -> bool:
return os.path.isfile(self.save_path) and 'debug' not in self.name
@property
def in_nf(self):
return 4 * (self.nb_features * 2 + 1)
@property
def debug(self):
return 'debug' in self.tag.lower()
@property
def out_nf(self):
return 3
def items(self) -> Iterator[str]:
return filter(lambda a: not a.startswith('__') and not callable(getattr(self, a)), dir(self))
def as_dict(self) -> dict:
return {item: getattr(self, item) for item in self.items()}
def save(self):
if os.path.isdir(self.cp_folder) and not self.already_saved:
with open(self.save_path, 'wb') as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
with open(self.save_path[:-3] + 'json', 'w') as f:
f.write(json.dumps(self.as_dict(), sort_keys=True, indent=4))
def load(self):
if self.already_saved:
with open(self.save_path, 'rb') as f:
loaded = pickle.load(f)
print(f'loading options from {self.save_path}')
return backward_compatibility(loaded)
return backward_compatibility(self)
def fill_args(self, **kwargs):
for arg in kwargs:
if hasattr(self, arg):
setattr(self, arg, kwargs[arg])
@staticmethod
def in_notebook() -> bool:
if len(sys.argv) < 2:
return False
return reduce(lambda x, y: x or y, map(lambda x: 'jupyter' in x, sys.argv[1:]))
def parse_cmdline(self):
if self.in_notebook():
return
parser = argparse.ArgumentParser(description='DGTS options')
parser.add_argument('--tag', type=str, help='')
parser.add_argument('--mesh-name', type=str, help='')
parser.add_argument('--template-name', type=str, default='sphere', help='')
parser.add_argument('--num-levels', type=int, help='')
parser.add_argument('--start-level', type=int, default=0, help='')
# inference options
parser.add_argument('--gen-mode', type=str, choices=['generate', 'animate'])
parser.add_argument('--num-gen-samples', type=int, default=8)
parser.add_argument('--target', type=str, default='fertility_al', help='')
parser.add_argument('--gen-levels', nargs='+', type=int, default=[1, 4], help='')
# gt optimization options
parser.add_argument('--template-start', type=int, default=0, help='')
parser = parser.parse_args().__dict__
args = {key: item for key, item in parser.items() if item is not None}
self.fill_args(**args)
def __init__(self, **kwargs):
self.tag = 'demo'
self.mesh_name = 'sphere_rail'
self.template_name = 'sphere'
self.start_level = 0
self.num_levels = 5
self.start_nf, self.min_nf, self.max_nf, = 32, 32, 128
self.num_layers = 7
self.scale_vs_factor = 2
self.noise_amplitude = 0.1
self.noise_before = True
self.update_axes = False
self.nb_features = False
self.fix_vs_noise = True
self.gen_mode, self.num_gen_samples, self.target, self.gen_levels = None, None, None, None
self.fill_args(**kwargs)
class TrainOption(Options):
def fill_args(self, **kwargs):
super(TrainOption, self).fill_args(**kwargs)
self.level_iters = [2000] * (self.num_levels + self.start_level)
def __init__(self, **kwargs):
super(TrainOption, self).__init__()
self.lr = 5e-4
self.betas = (.5, .99)
self.lr_decay = 0.5
self.lr_decay_every = 500
self.export_meshes_every = 400
self.discriminator_iters = 2
self.generator_iters = 3
self.reconstruction_weight = 5
self.penalty_weight = 0.1
self.inside_out = False
self.level_iters = [2000] * (self.num_levels + self.start_level)
self.reconstruction_start = 1
self.fill_args(**kwargs)
class GtOptions(TrainOption):
def fill_args(self, **kwargs):
super(TrainOption, self).fill_args(**kwargs)
self.level_iters = [3000] * (max(self.num_levels, 1) + self.start_level)
self.num_samples = [3000 * (i + 1) for i in range(max(self.num_levels, 1) + self.start_level)]
def __init__(self, **kwargs):
super(GtOptions, self).__init__()
self.template_start = 0
self.lr = 1e-4
self.gamma_edge_global = 5e-3
self.gamma_edge_local = 5e-4
self.gamma_gravity = 0.01
self.gamma_noraml_nb = 0
self.gamma_distance_s2t = 0.4
self.gamma_distance_t2s = 0.6
self.gamma_noraml_s2t = 0.1
self.gamma_noraml_t2s = 0.1
self.ch_iters = 2
self.triangulation_iters = 3
self.parse_cmdline()
self.pre_template = True
self.switches = ()
self.fill_args(**kwargs)
self.level_iters = [3000] * (max(self.num_levels, 1) + self.start_level)
self.num_samples = [3000 * (i + 1) for i in range(max(self.num_levels, 1) + self.start_level)]
@property
def cp_folder(self) -> str:
return f'{const.DATA_ROOT}/{self.mesh_name}/'
@property
def triangulation_weights(self):
return self.gamma_edge_global, self.gamma_edge_local, self.gamma_gravity
@property
def chamfer_weights(self):
return self.gamma_distance_s2t, self.gamma_distance_t2s, self.gamma_noraml_s2t, self.gamma_noraml_t2s
def backward_compatibility(opt: Options) -> Options:
defaults = {}
for key, value in defaults.items():
if not hasattr(opt, key):
setattr(opt, key, value)
return opt
def copy(opt: Options) -> Options:
opt_copy = opt.__class__()
for item in opt_copy.items():
if hasattr(opt, item):
try:
setattr(opt_copy, item, getattr(opt, item))
except:
continue
return opt_copy