def get_callbacks(self, stage: str) -> "OrderedDict[str, Callback]":
"""
Override of ``BaseExperiment.get_callbacks`` method.
Will add several of callbacks by default in case they missed.
Args:
stage: name of stage. It should start with `infer` if you
don't need default callbacks, as they required only for
training stages.
Returns:
OrderedDict[str, Callback]: Ordered dictionary of callbacks
for experiment
"""
callbacks = super().get_callbacks(stage=stage) or OrderedDict()
# default_callbacks = [(Name, InterfaceClass, InstanceFactory)]
default_callbacks = []
is_amp_enabled = (
self.distributed_params.get("amp", False) and check_amp_available()
)
optimizer_cls = (
AMPOptimizerCallback if is_amp_enabled else OptimizerCallback
)
if not stage.startswith("infer"):
if self._criterion is not None and isinstance(
self._criterion, Criterion
):
default_callbacks.append(
("_criterion", None, CriterionCallback)
)
if self._optimizer is not None and isinstance(
self._optimizer, Optimizer
):
default_callbacks.append(
("_optimizer", IOptimizerCallback, optimizer_cls)
)
if self._scheduler is not None and isinstance(
self._scheduler, (Scheduler, ReduceLROnPlateau)
):
default_callbacks.append(
("_scheduler", ISchedulerCallback, SchedulerCallback)
)
for (
callback_name,
callback_interface,
callback_fn,
) in default_callbacks:
callback_interface = callback_interface or callback_fn
is_already_present = any(
check_callback_isinstance(x, callback_interface)
for x in callbacks.values()
)
if not is_already_present:
callbacks[callback_name] = callback_fn()
return callbacks
def get_callbacks(self, stage: str) -> "OrderedDict[Callback]":
"""Returns the callbacks for a given stage."""
callbacks_params = self.stages_config[stage].get(
"callbacks_params", {})
callbacks = OrderedDict()
for key, callback_params in callbacks_params.items():
callback = self._get_callback(**callback_params)
callbacks[key] = callback
# default_callbacks = [(Name, InterfaceClass, InstanceFactory)]
default_callbacks = []
is_amp_enabled = (self.distributed_params.get("amp", False)
and check_amp_available())
optimizer_cls = (AMPOptimizerCallback
if is_amp_enabled else OptimizerCallback)
if self._verbose:
default_callbacks.append(("_verbose", None, VerboseLogger))
if self._check_time:
default_callbacks.append(("_timer", None, TimerCallback))
if self._check_run:
default_callbacks.append(("_check", None, CheckRunCallback))
if self._overfit:
default_callbacks.append(("_overfit", None, BatchOverfitCallback))
if not stage.startswith("infer"):
default_callbacks.append(("_metrics", None, MetricManagerCallback))
default_callbacks.append(
("_validation", None, ValidationManagerCallback))
default_callbacks.append(("_console", None, ConsoleLogger))
if self.logdir is not None:
default_callbacks.append(("_saver", None, CheckpointCallback))
default_callbacks.append(
("_tensorboard", None, TensorboardLogger))
if self.stages_config[stage].get("criterion_params", {}):
default_callbacks.append(
("_criterion", None, CriterionCallback))
if self.stages_config[stage].get("optimizer_params", {}):
default_callbacks.append(
("_optimizer", IOptimizerCallback, optimizer_cls))
if self.stages_config[stage].get("scheduler_params", {}):
default_callbacks.append(
("_scheduler", ISchedulerCallback, SchedulerCallback))
default_callbacks.append(("_exception", None, ExceptionCallback))
for (
callback_name,
callback_interface,
callback_fn,
) in default_callbacks:
callback_interface = callback_interface or callback_fn
is_already_present = any(
check_callback_isinstance(x, callback_interface)
for x in callbacks.values())
if not is_already_present:
callbacks[callback_name] = callback_fn()
# NOTE: stage should be in self.stages_config
# othervise will be raised ValueError
stage_index = list(self.stages_config.keys()).index(stage)
self._process_callbacks(callbacks, stage_index)
return callbacks