def get_callbacks(self, stage: str) -> "OrderedDict[str, Callback]":
"""Returns the callbacks for a given stage."""
callbacks_params = get_by_keys(self._stage_config,
stage,
"callbacks",
default={})
callbacks = OrderedDict(REGISTRY.get_from_params(**callbacks_params))
is_callback_exists = lambda callback_fn: any(
callback_isinstance(x, callback_fn) for x in callbacks.values())
if self._verbose and not is_callback_exists(TqdmCallback):
callbacks["_verbose"] = TqdmCallback()
if self._timeit and not is_callback_exists(TimerCallback):
callbacks["_timer"] = TimerCallback()
if self._check and not is_callback_exists(CheckRunCallback):
callbacks["_check"] = CheckRunCallback()
if self._overfit and not is_callback_exists(BatchOverfitCallback):
callbacks["_overfit"] = BatchOverfitCallback()
if self._logdir is not None and not is_callback_exists(
ICheckpointCallback):
callbacks["_checkpoint"] = CheckpointCallback(logdir=os.path.join(
self._logdir, "checkpoints"), )
return callbacks
def get_callbacks(self, stage: str) -> "OrderedDict[str, Callback]":
"""Returns the callbacks for a given stage."""
callbacks_params = self._config.stages[stage].callbacks or {}
callbacks: Dict[str, Callback] = {
name: self._get_callback_from_params(callback_params)
for name, callback_params in callbacks_params.items()
}
is_callback_exists = lambda callback_fn: any(
callback_isinstance(x, callback_fn) for x in callbacks.values()
)
if self._verbose and not is_callback_exists(TqdmCallback):
callbacks["_verbose"] = TqdmCallback()
if self._timeit and not is_callback_exists(TimerCallback):
callbacks["_timer"] = TimerCallback()
if self._check and not is_callback_exists(CheckRunCallback):
callbacks["_check"] = CheckRunCallback()
if self._overfit and not is_callback_exists(BatchOverfitCallback):
callbacks["_overfit"] = BatchOverfitCallback()
if self._logdir is not None and not is_callback_exists(ICheckpointCallback):
callbacks["_checkpoint"] = CheckpointCallback(
logdir=os.path.join(self._logdir, "checkpoints")
)
return callbacks
def get_callbacks(self) -> "OrderedDict[str, Callback]":
"""Returns the callbacks for the experiment."""
callbacks = sort_callbacks_by_order(self._callbacks)
callback_exists = lambda callback_fn: any(
callback_isinstance(x, callback_fn) for x in callbacks.values()
)
if self._verbose and not callback_exists(TqdmCallback):
callbacks["_verbose"] = TqdmCallback()
if self._timeit and not callback_exists(TimerCallback):
callbacks["_timer"] = TimerCallback()
if self._check and not callback_exists(CheckRunCallback):
callbacks["_check"] = CheckRunCallback()
if self._overfit and not callback_exists(BatchOverfitCallback):
callbacks["_overfit"] = BatchOverfitCallback()
if self._profile and not callback_exists(ProfilerCallback):
callbacks["_profile"] = ProfilerCallback(
tensorboard_path=os.path.join(self._logdir, "tb_profile"),
profiler_kwargs={
"activities": [
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
"on_trace_ready": torch.profiler.tensorboard_trace_handler(
os.path.join(self._logdir, "tb_profile")
),
"with_stack": True,
"with_flops": True,
},
)
if self._logdir is not None and not callback_exists(ICheckpointCallback):
callbacks["_checkpoint"] = CheckpointCallback(
logdir=os.path.join(self._logdir, "checkpoints"),
loader_key=self._valid_loader,
metric_key=self._valid_metric,
minimize=self._minimize_valid_metric,
load_best_on_end=self._load_best_on_end,
)
return callbacks
def get_callbacks(self, stage: str) -> "OrderedDict[str, Callback]":
"""Returns the callbacks for a given stage."""
callbacks = sort_callbacks_by_order(self._callbacks)
is_callback_exists = lambda callback_fn: any(
callback_isinstance(x, callback_fn) for x in callbacks.values()
)
if self._verbose and not is_callback_exists(TqdmCallback):
callbacks["_verbose"] = TqdmCallback()
if self._timeit and not is_callback_exists(TimerCallback):
callbacks["_timer"] = TimerCallback()
if self._check and not is_callback_exists(CheckRunCallback):
callbacks["_check"] = CheckRunCallback()
if self._overfit and not is_callback_exists(BatchOverfitCallback):
callbacks["_overfit"] = BatchOverfitCallback()
if self._logdir is not None and not is_callback_exists(ICheckpointCallback):
callbacks["_checkpoint"] = CheckpointCallback(
logdir=os.path.join(self._logdir, "checkpoints"),
loader_key=self._valid_loader,
metric_key=self._valid_metric,
minimize=self._minimize_valid_metric,
)
return callbacks