torch_mist.utils.logging

Subpackages

• torch_mist.utils.logging.logger
  • torch_mist.utils.logging.logger.base
  • torch_mist.utils.logging.logger.pandas
  • torch_mist.utils.logging.logger.wandb

Submodules

• torch_mist.utils.logging.metrics

Package Contents

Classes

PandasLogger
Logger

class torch_mist.utils.logging.PandasLogger(log_dir: str = '.', log_name: str | None = None, log_every: int = 1)

Bases: torch_mist.utils.logging.logger.base.Logger

_log(data: Any, name: str, iteration: int, epoch: int, split: str)

get_log() → pandas.DataFrame

_reset_log()

_save_log(log: pandas.DataFrame)

class torch_mist.utils.logging.Logger(log_dir: str = '.', log_every: int = 1)

train()

valid()

test()

iteration()

epoch()

on_split_start(name: str)

on_split_end(name: str)

on_iteration_start()

on_iteration_end()

on_epoch_start()

on_epoch_end()

logged_methods(instance: Any, methods: List[str | Tuple[str, Callable[[Any, Any], Any]]])

add_logging_hook(instance: Any, method_name: str, metric: Callable[[Any, Any], Dict[str, Any]])

detach_hook(method_name)

is_logged(method_name: str) → bool

_log_buffer(force_logging: str | None = None)

log(name: str, data_dict: Any)

abstract _log(data: Any, name: str, iteration: int, epoch: int, split: str)

abstract _reset_log()

reset_log()

detach_all_hooks()

clear()

__del__()

get_log() → Any | None

_save_log(log: Any)

save_log()

abstract save_model(model: torch.nn.Module, name: str)