Spaces:
Running
Running
File size: 1,514 Bytes
4f6613a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
from typing import List
import hydra
from omegaconf import DictConfig
from pytorch_lightning import Callback
from pytorch_lightning.loggers import Logger
from .logger import RankedLogger
log = RankedLogger(__name__, rank_zero_only=True)
def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:
"""Instantiates callbacks from config."""
callbacks: List[Callback] = []
if not callbacks_cfg:
log.warning("No callback configs found! Skipping..")
return callbacks
if not isinstance(callbacks_cfg, DictConfig):
raise TypeError("Callbacks config must be a DictConfig!")
for _, cb_conf in callbacks_cfg.items():
if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf:
log.info(f"Instantiating callback <{cb_conf._target_}>")
callbacks.append(hydra.utils.instantiate(cb_conf))
return callbacks
def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]:
"""Instantiates loggers from config."""
logger: List[Logger] = []
if not logger_cfg:
log.warning("No logger configs found! Skipping...")
return logger
if not isinstance(logger_cfg, DictConfig):
raise TypeError("Logger config must be a DictConfig!")
for _, lg_conf in logger_cfg.items():
if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:
log.info(f"Instantiating logger <{lg_conf._target_}>")
logger.append(hydra.utils.instantiate(lg_conf))
return logger
|