espnet2.train.reporter.Reporter

source

class espnet2.train.reporter.Reporter(epoch: int = 0)

Bases: object

Reporter class.

##

Example

s

>>> reporter = Reporter()
>>> with reporter.observe('train') as sub_reporter:
...     for batch in iterator:
...         stats = dict(loss=0.2)
...         sub_reporter.register(stats)

check_early_stopping(patience: int, key1: str, key2: str, mode: str, epoch: int | None = None, logger=None) → bool

finish_epoch(sub_reporter: SubReporter) → None

get_all_keys(epoch: int | None = None) → Tuple[Tuple[str, str], ...]

get_best_epoch(key: str, key2: str, mode: str, nbest: int = 0) → int

get_epoch() → int

get_keys(epoch: int | None = None) → Tuple[str, ...]

Returns keys1 e.g. train,eval.

get_keys2(key: str, epoch: int | None = None) → Tuple[str, ...]

Returns keys2 e.g. loss,acc.

get_value(key: str, key2: str, epoch: int | None = None)

has(key: str, key2: str, epoch: int | None = None) → bool

load_state_dict(state_dict: dict)

log_message(epoch: int | None = None) → str

matplotlib_plot(output_dir: str | Path)

Plot stats using Matplotlib and save images.

observe(key: str, epoch: int = None) → ContextManager[SubReporter]

set_epoch(epoch: int) → None

sort_epochs(key: str, key2: str, mode: str) → List[int]

sort_epochs_and_values(key: str, key2: str, mode: str) → List[Tuple[int, float]]

Return the epoch which resulted the best value.

Example

>>> val = reporter.sort_epochs_and_values('eval', 'loss', 'min')
>>> e_1best, v_1best = val[0]
>>> e_2best, v_2best = val[1]

sort_values(key: str, key2: str, mode: str) → List[float]

start_epoch(key: str, epoch: int | None = None) → SubReporter

state_dict()

tensorboard_add_scalar(summary_writer, epoch: int | None = None, key1: str | None = None)

wandb_log(epoch: int | None = None)