def on_epoch_end(self, runner: "IRunner"):
"""
Logs metrics here
Args:
runner: runner for experiment
"""
if runner.logdir is None:
return
per_loader_metrics = split_dict_to_subdicts(
dct=runner.epoch_metrics,
prefixes=list(runner.loaders.keys()),
extra_key="_base",
)
for loader_key, per_loader_metrics in per_loader_metrics.items():
if "base" in loader_key:
continue
if not self.header_created[loader_key]:
self._make_header(
metrics=per_loader_metrics, loader_key=loader_key
)
self.header_created[loader_key] = True
self._log_metrics(
metrics=per_loader_metrics,
step=runner.global_epoch,
loader_key=loader_key,
)
def _format_message(self, runner: "IRunner"):
message = [""]
mode_metrics = split_dict_to_subdicts(
dct=runner.epoch_metrics,
prefixes=list(runner.loaders.keys()),
extra_key="_base",
)
metrics = self._format_metrics(mode_metrics)
for key, value in metrics.items():
message.append(f"{runner.epoch}/{runner.num_epochs} "
f"* Epoch {runner.global_epoch} ({key}): {value}")
message = "\n".join(message)
return message
def on_epoch_end(self, runner: "IRunner"):
"""Translate epoch metrics to Weights & Biases."""
extra_mode = "_base"
splitted_epoch_metrics = split_dict_to_subdicts(
dct=runner.epoch_metrics,
prefixes=list(runner.loaders.keys()),
extra_key=extra_mode,
)
if self.log_on_epoch_end:
if extra_mode in splitted_epoch_metrics.keys():
# if we are using OptimizerCallback
self._log_metrics(
metrics=splitted_epoch_metrics[extra_mode],
step=runner.global_epoch,
mode=extra_mode,
suffix=self.epoch_log_suffix,
commit=True,
)
def on_epoch_end(self, runner: "IRunner"):
"""Translate epoch metrics to tensorboard."""
if runner.logdir is None:
return
if self.log_on_epoch_end:
per_mode_metrics = split_dict_to_subdicts(
dct=runner.epoch_metrics,
prefixes=list(runner.loaders.keys()),
extra_key="_base",
)
for mode, metrics in per_mode_metrics.items():
# suffix = "" if mode == "_base" else "/epoch"
self._log_metrics(
metrics=metrics,
step=runner.global_epoch,
mode=mode,
suffix="/epoch",
)
for logger in self.loggers.values():
logger.flush()