def cache_result(self) -> None:
"""
This function is called after every hook and stores the result object
"""
with self.trainer.profiler.profile("cache_result"):
model_ref = self.trainer.lightning_module
# extract hook results
hook_result = model_ref._results
if len(hook_result) == 1:
model_ref._current_fx_name = None
return
info = self.info
fx_name = info["fx_name"]
self._internals.setdefault(fx_name, HookResultStore(fx_name))
# attach capture batch_size
Result.attach_batch_size(self._batch_size, hook_result)
hook_result = hook_result.detach()
if self.trainer.move_metrics_to_cpu:
hook_result = hook_result.cpu()
elif self.trainer._distrib_type == DistributedType.DP:
hook_result = hook_result.to(torch.device("cuda", self.trainer.root_gpu))
self._internals[fx_name].append(hook_result, info)
# update logged_metrics, progress_bar_metrics, callback_metrics
if "epoch_end" in fx_name:
self.update_logger_connector()
self.reset_model()
def cache_result(self) -> None:
"""
This function is called after every hook
and store the result object
"""
with self.trainer.profiler.profile("cache_result"):
model_ref = self.trainer.get_model()
# extract hook results
hook_result = model_ref._results
if len(hook_result) == 1:
model_ref._current_hook_fx_name = None
model_ref._current_fx_name = ''
return
# extract model information
fx_name, dataloader_idx = self.current_model_info()
# add only if anything as been logged
# default len is 1 due to _internals
if fx_name not in self._internals:
self._internals[fx_name] = HookResultStore(fx_name)
extra_info = {}
if self.has_split_and_opt_idx:
extra_info = self.extra_info
# attach capture batch_size
Result.attach_batch_size(self._batch_size, hook_result)
hook_result.detach()
if self.trainer.move_metrics_to_cpu:
hook_result.cpu()
elif self.trainer.use_dp:
hook_result.to(torch.device("cuda", self.trainer.root_gpu))
self._internals[fx_name].append(hook_result,
dataloader_idx=dataloader_idx,
extra_info=extra_info)
# update logged_metrics, progress_bar_metrics, callback_metrics
self.update_logger_connector(fx_name)
self.reset_model()
def cache_result(self) -> None:
"""
This function is called after every hook
and store the result object
"""
model_ref = self.trainer.get_model()
# extract hook results
hook_result = model_ref._results
# extract model information
fx_name, dataloader_idx = self.current_model_info()
# add only if anything as been logged
# default len is 1 due to _internals
if len(hook_result) > 1:
if fx_name not in self._internals:
self._internals[fx_name] = HookResultStore(fx_name)
extra_info = {}
if self.has_split_and_opt_idx:
extra_info = self.extra_info
# attach capture batch_size
Result.attach_batch_size(self._batch_size, hook_result)
self._internals[fx_name].append(deepcopy(hook_result),
dataloader_idx=dataloader_idx,
extra_info=extra_info)
# update logged_metrics, progress_bar_metrics, callback_metrics
self.update_logger_connector(fx_name)
# reset _results, fx_name
self.reset_model()
