def _track_callback_metrics(self, eval_results):
if len(eval_results) > 0 and (eval_results[0] is None or not isinstance(eval_results[0], Result)):
return
flat = {}
if isinstance(eval_results, list):
for eval_result in eval_results:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_result, torch.Tensor):
flat = {'val_loss': eval_result}
elif isinstance(eval_result, dict):
flat = flatten_dict(eval_result)
# removing val_loss magic word to map to checkpoint + ES callback
if 'val_loss' in flat:
flat['checkpoint_on'] = flat['val_loss']
flat['early_stop_on'] = flat['val_loss']
self.trainer.logger_connector.callback_metrics.update(flat)
if self.trainer.testing:
self.trainer.logger_connector.evaluation_callback_metrics.update(flat)
else:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_results, torch.Tensor):
flat = {'val_loss': eval_results}
else:
flat = flatten_dict(eval_results)
# removing val_loss magic word to map to checkpoint + ES callback
if 'val_loss' in flat:
flat['checkpoint_on'] = flat['val_loss']
flat['early_stop_on'] = flat['val_loss']
self.trainer.logger_connector.callback_metrics.update(flat)
if self.trainer.testing:
self.trainer.logger_connector.evaluation_callback_metrics.update(flat)
def _track_callback_metrics(self, eval_results):
if len(eval_results) > 0 and (eval_results[0] is None or
not isinstance(eval_results[0], Result)):
return
flat = {}
if isinstance(eval_results, list):
for eval_result in eval_results:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_result, torch.Tensor):
flat = {'val_loss': eval_result}
elif isinstance(eval_result, dict):
flat = flatten_dict(eval_result)
self.trainer.logger_connector.callback_metrics.update(flat)
if self.trainer.state in (TrainerState.TESTING,
TrainerState.VALIDATING):
self.trainer.logger_connector.evaluation_callback_metrics.update(
flat)
else:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_results, torch.Tensor):
flat = {'val_loss': eval_results}
else:
flat = flatten_dict(eval_results)
self.trainer.logger_connector.callback_metrics.update(flat)
if self.trainer.state in (TrainerState.TESTING,
TrainerState.VALIDATING):
self.trainer.logger_connector.evaluation_callback_metrics.update(
flat)
def _log_on_evaluation_epoch_end_metrics(self, eval_results,
using_eval_result):
if len(eval_results) > 0 and eval_results[0] is None:
return
if using_eval_result:
if isinstance(eval_results, list):
for eval_result in eval_results:
self.trainer.logger_connector.callback_metrics.update(
eval_result.callback_metrics)
else:
self.trainer.logger_connector.callback_metrics.update(
eval_results.callback_metrics)
else:
if isinstance(eval_results, list):
for eval_result in eval_results:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_result, torch.Tensor):
flat = {'val_loss': eval_result}
else:
flat = flatten_dict(eval_result)
# removing val_loss magic word to map to checkpoint + ES callback
if 'val_loss' in flat:
flat['checkpoint_on'] = flat['val_loss']
flat['early_stop_on'] = flat['val_loss']
self.trainer.logger_connector.callback_metrics.update(flat)
else:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_results, torch.Tensor):
flat = {'val_loss': eval_results}
else:
flat = flatten_dict(eval_results)
# removing val_loss magic word to map to checkpoint + ES callback
if 'val_loss' in flat:
flat['checkpoint_on'] = flat['val_loss']
flat['early_stop_on'] = flat['val_loss']
self.trainer.logger_connector.callback_metrics.update(flat)
def __update_callback_metrics(self, eval_results, using_eval_result):
if using_eval_result:
if isinstance(eval_results, list):
for eval_result in eval_results:
self.callback_metrics = eval_result.callback_metrics
else:
self.callback_metrics = eval_results.callback_metrics
else:
if isinstance(eval_results, list):
for eval_result in eval_results:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_result, torch.Tensor):
flat = {'val_loss': eval_result}
else:
flat = flatten_dict(eval_result)
self.callback_metrics.update(flat)
else:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_results, torch.Tensor):
flat = {'val_loss': eval_results}
else:
flat = flatten_dict(eval_results)
self.callback_metrics.update(flat)
def on_evaluation_epoch_end(self, eval_results, using_eval_result):
if using_eval_result:
if isinstance(eval_results, list):
for eval_result in eval_results:
self.trainer.logger_connector.callback_metrics = eval_result.callback_metrics
else:
self.trainer.logger_connector.callback_metrics = eval_results.callback_metrics
else:
if isinstance(eval_results, list):
for eval_result in eval_results:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_result, torch.Tensor):
flat = {'val_loss': eval_result}
else:
flat = flatten_dict(eval_result)
self.trainer.logger_connector.callback_metrics.update(flat)
else:
# with a scalar return, auto set it to "val_loss" for callbacks
if isinstance(eval_results, torch.Tensor):
flat = {'val_loss': eval_results}
else:
flat = flatten_dict(eval_results)
self.trainer.logger_connector.callback_metrics.update(flat)
