def on_stage_start(self, runner: "IRunner") -> None:
"""Event handler."""
self.model = get_attr(runner, key="model", inner_key=self.model_key)
self.optimizer = get_attr(runner,
key="optimizer",
inner_key=self.optimizer_key)
assert self.model is not None
assert self.optimizer is not None
def on_stage_start(self, runner: "IRunner") -> None:
"""Stage start hook.
Args:
runner: current runner
"""
self.scheduler = get_attr(runner,
key="scheduler",
inner_key=self.scheduler_key)
assert self.scheduler is not None
if isinstance(self.scheduler,
torch.optim.lr_scheduler.ReduceLROnPlateau):
assert self.loader_key is not None and self.metric_key is not None, (
"For `ReduceLROnPlateau` scheduler `SchedulerCallback` "
"required both `loader_key` and `metric_key` specified")
if self.mode is None:
if isinstance(self.scheduler, BatchScheduler):
self.mode = "batch"
else:
self.mode = "epoch"
if isinstance(self.scheduler,
OneCycleLRWithWarmup) and self.mode == "batch":
self.scheduler.reset()
assert self.mode is not None
def on_experiment_start(self, runner: "IRunner") -> None:
"""Event handler."""
self.optimizer = optimizer = get_attr(
runner, key="optimizer", inner_key=self.optimizer_key
)
self.optimizer = optimizer
self.init_lr = optimizer.param_groups[0]["lr"]
def on_stage_start(self, runner: "IRunner") -> None:
"""Stage start hook.
Args:
runner: current runner
"""
self.reduced_metric = self.reduced_metric or runner.main_metric
scheduler = get_attr(
runner, key="scheduler", inner_key=self.scheduler_key
)
assert scheduler is not None
self._scheduler = scheduler
if self.mode is None:
if isinstance(scheduler, BatchScheduler):
self.mode = "batch"
else:
self.mode = "epoch"
if (
isinstance(scheduler, OneCycleLRWithWarmup)
and self.mode == "batch"
):
scheduler.reset()
assert self.mode is not None
def on_stage_start(self, runner: "IRunner"):
"""Checks that the current stage has correct criterion.
Args:
runner: current runner
"""
self.criterion = get_attr(runner, key="criterion", inner_key=self.criterion_key)
assert self.criterion is not None
def on_stage_start(self, runner: "IRunner") -> None:
"""Stage start hook.
Args:
runner: current runner
"""
self.optimizer = optimizer = get_attr(runner,
key="optimizer",
inner_key=self.optimizer_key)
self.optimizer = optimizer
self.init_lr = optimizer.param_groups[0]["lr"]
def on_stage_start(self, runner: "IRunner") -> None:
"""Stage start hook.
Args:
runner: current runner
"""
optimizer = get_attr(runner,
key="optimizer",
inner_key=self.optimizer_key)
assert optimizer is not None
self._optimizer = optimizer
self.init_lr = optimizer.defaults["lr"]
def on_stage_start(self, runner: "IRunner") -> None:
"""Checks that the current stage has correct optimizer.
Args:
runner(IRunner): current runner
"""
from torch.cuda.amp import GradScaler
self._optimizer = get_attr(runner,
key="optimizer",
inner_key=self.optimizer_key)
self.scaler = GradScaler()
assert self._optimizer is not None
def on_stage_start(self, runner: "IRunner") -> None:
"""Checks that the current stage has correct optimizer.
Args:
runner(IRunner): current runner
"""
self._optimizer = get_attr(runner,
key="optimizer",
inner_key=self.optimizer_key)
# device based optimization step
if runner.device.type == "xla":
self._optimizer_step_fn = self._optimizer_step_tpu
else:
self._optimizer_step_fn = self._optimizer_step
assert self._optimizer is not None
def on_stage_start(self, runner: "IRunner") -> None:
"""Resolve amp/apex settings, prepare optimizer and scaler
Args:
runner(IRunner): current runner
"""
if self.use_amp is None:
if runner.experiment is not None:
self.use_amp = runner.experiment.distributed_params.get(
"amp", False)
else:
self.use_amp = False
if self.use_apex is None:
if runner.experiment is not None:
self.use_apex = runner.experiment.distributed_params.get(
"apex", False)
else:
self.use_apex = False
self._optimizer = get_attr(runner,
key="optimizer",
inner_key=self.optimizer_key)
# device based optimization step
if runner.device.type == "xla":
self._optimizer_step_fn = self._optimizer_step_tpu
elif self.use_amp:
self._optimizer_step_fn = self._optimizer_step_amp
else:
self._optimizer_step_fn = self._optimizer_step
if hasattr(self._optimizer, "_amp_stash") and not self.use_apex:
warnings.warn(
"`_amp_stash` is found in `self._optimizer`:, "
"but `use_apex` is False",
stacklevel=2,
)
assert self._optimizer is not None
if self.use_amp:
from torch.cuda.amp import GradScaler
self.scaler = GradScaler()
def on_experiment_start(self, runner: "IRunner") -> None:
"""Event handler."""
self.optimizer = get_attr(runner, key="optimizer", inner_key=self.optimizer_key)
assert self.optimizer is not None
def on_experiment_start(self, runner: "IRunner"):
"""Event handler."""
self.criterion = get_attr(runner,
key="criterion",
inner_key=self.criterion_key)
assert self.criterion is not None
