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import os
from dataclasses import dataclass, field
from datetime import datetime
from omegaconf import OmegaConf
import threestudio
from threestudio.utils.typing import *
# ============ Register OmegaConf Recolvers ============= #
OmegaConf.register_new_resolver(
"calc_exp_lr_decay_rate", lambda factor, n: factor ** (1.0 / n)
)
OmegaConf.register_new_resolver("add", lambda a, b: a + b)
OmegaConf.register_new_resolver("sub", lambda a, b: a - b)
OmegaConf.register_new_resolver("mul", lambda a, b: a * b)
OmegaConf.register_new_resolver("div", lambda a, b: a / b)
OmegaConf.register_new_resolver("idiv", lambda a, b: a // b)
OmegaConf.register_new_resolver("basename", lambda p: os.path.basename(p))
OmegaConf.register_new_resolver("rmspace", lambda s, sub: s.replace(" ", sub))
OmegaConf.register_new_resolver("tuple2", lambda s: [float(s), float(s)])
OmegaConf.register_new_resolver("gt0", lambda s: s > 0)
OmegaConf.register_new_resolver("cmaxgt0", lambda s: C_max(s) > 0)
OmegaConf.register_new_resolver("not", lambda s: not s)
OmegaConf.register_new_resolver(
"cmaxgt0orcmaxgt0", lambda a, b: C_max(a) > 0 or C_max(b) > 0
)
# ======================================================= #
def C_max(value: Any) -> float:
if isinstance(value, int) or isinstance(value, float):
pass
else:
value = config_to_primitive(value)
if not isinstance(value, list):
raise TypeError("Scalar specification only supports list, got", type(value))
if len(value) == 3:
value = [0] + value
assert len(value) == 4
start_step, start_value, end_value, end_step = value
value = max(start_value, end_value)
return value
@dataclass
class ExperimentConfig:
name: str = "default"
description: str = ""
tag: str = ""
seed: int = 0
use_timestamp: bool = True
timestamp: Optional[str] = None
exp_root_dir: str = "outputs"
### these shouldn't be set manually
exp_dir: str = "outputs/default"
trial_name: str = "exp"
trial_dir: str = "outputs/default/exp"
n_gpus: int = 1
###
resume: Optional[str] = None
data_type: str = ""
data: dict = field(default_factory=dict)
system_type: str = ""
system: dict = field(default_factory=dict)
# accept pytorch-lightning trainer parameters
# see https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api
trainer: dict = field(default_factory=dict)
# accept pytorch-lightning checkpoint callback parameters
# see https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html#modelcheckpoint
checkpoint: dict = field(default_factory=dict)
def __post_init__(self):
if not self.tag and not self.use_timestamp:
raise ValueError("Either tag is specified or use_timestamp is True.")
self.trial_name = self.tag
# if resume from an existing config, self.timestamp should not be None
if self.timestamp is None:
self.timestamp = ""
if self.use_timestamp:
if self.n_gpus > 1:
threestudio.warn(
"Timestamp is disabled when using multiple GPUs, please make sure you have a unique tag."
)
else:
self.timestamp = datetime.now().strftime("@%Y%m%d-%H%M%S")
self.trial_name += self.timestamp
self.exp_dir = os.path.join(self.exp_root_dir, self.name)
self.trial_dir = os.path.join(self.exp_dir, self.trial_name)
os.makedirs(self.trial_dir, exist_ok=True)
def load_config(*yamls: str, cli_args: list = [], from_string=False, **kwargs) -> Any:
if from_string:
yaml_confs = [OmegaConf.create(s) for s in yamls]
else:
yaml_confs = [OmegaConf.load(f) for f in yamls]
cli_conf = OmegaConf.from_cli(cli_args)
cfg = OmegaConf.merge(*yaml_confs, cli_conf, kwargs)
OmegaConf.resolve(cfg)
assert isinstance(cfg, DictConfig)
scfg = parse_structured(ExperimentConfig, cfg)
return scfg
def config_to_primitive(config, resolve: bool = True) -> Any:
return OmegaConf.to_container(config, resolve=resolve)
def dump_config(path: str, config) -> None:
with open(path, "w") as fp:
OmegaConf.save(config=config, f=fp)
def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any:
scfg = OmegaConf.structured(fields(**cfg))
return scfg
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