def get_logger(self, cfg: DictConfig,
save_dir: Path) -> pl_loggers.WandbLogger:
"""Returns the Weights and Biases (wandb) logger object (really an wandb Run object)
The run object corresponds to a single execution of the script and is returned from `wandb.init()`.
Args:
run_id: Unique run id. If run id exists, will continue logging to that run.
cfg: The entire config got from hydra, for purposes of logging the config of each run in wandb.
save_dir: Root dir to save wandb log files
Returns:
wandb.wandb_sdk.wandb_run.Run: wandb run object. Can be used for logging.
"""
# Some argument names to wandb are different from the attribute names of the class.
# Pop the offending attributes before passing to init func.
args_dict = asdict_filtered(self)
run_name = args_dict.pop("run_name")
run_id = args_dict.pop("run_id")
# If `self.save_hyperparams()` is called in LightningModule, it will save the cfg passed as argument
# cfg_dict = OmegaConf.to_container(cfg, resolve=True)
wb_logger = pl_loggers.WandbLogger(name=run_name,
id=run_id,
save_dir=str(save_dir),
**args_dict)
return wb_logger
def get_trainer(self, pl_logger: LightningLoggerBase,
callbacks: List[Callback],
default_root_dir: str) -> pl.Trainer:
trainer = pl.Trainer(
logger=pl_logger,
callbacks=callbacks,
default_root_dir=default_root_dir,
**asdict_filtered(self),
)
return trainer
def get_optimizer(self, model_params) -> torch.optim.Optimizer:
return torch.optim.SGD(params=model_params, **asdict_filtered(self))
def get_scheduler(
self, optimizer: Optimizer
) -> torch.optim.lr_scheduler.ReduceLROnPlateau:
return torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, **asdict_filtered(self))
def get_scheduler(self,
optimizer: Optimizer) -> torch.optim.lr_scheduler.StepLR:
return torch.optim.lr_scheduler.StepLR(optimizer,
**asdict_filtered(self))
def get_scheduler(
self, optimizer: Optimizer) -> torch.optim.lr_scheduler.CyclicLR:
return torch.optim.lr_scheduler.CyclicLR(optimizer,
cycle_momentum=False,
**asdict_filtered(self))
def get_model(self) -> torch.nn.Module:
return DeepLab(**asdict_filtered(self))
def get_callback(self) -> Callback:
return LearningRateMonitor(**asdict_filtered(self))
def get_callback(self, exp_dir: str, cfg: DictConfig) -> Callback:
return LogMedia(exp_dir=exp_dir, cfg=cfg, **asdict_filtered(self))
def get_callback(self, logs_dir) -> Callback:
return ModelCheckpoint(dirpath=logs_dir, **asdict_filtered(self))
def get_callback(self) -> Callback:
return EarlyStopping(**asdict_filtered(self))
def get_datamodule(self) -> LaPaDataModule:
return LaPaDataModule(**asdict_filtered(self))