diff --git a/monai/handlers/mlflow_handler.py b/monai/handlers/mlflow_handler.py index 8c847e0521..97a46cc0b7 100644 --- a/monai/handlers/mlflow_handler.py +++ b/monai/handlers/mlflow_handler.py @@ -116,7 +116,7 @@ def close(self) -> None: def epoch_completed(self, engine: Engine) -> None: """ Handler for train or validation/evaluation epoch completed Event. - Track epoch level log, default values are from Ignite state.metrics dict. + Track epoch level log, default values are from Ignite `engine.state.metrics` dict. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. @@ -143,8 +143,8 @@ def iteration_completed(self, engine: Engine) -> None: def _default_epoch_log(self, engine: Engine) -> None: """ - Execute epoch level log operation based on Ignite engine.state data. - Track the values from Ignite state.metrics dict. + Execute epoch level log operation. + Default to track the values from Ignite `engine.state.metrics` dict. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. @@ -159,9 +159,10 @@ def _default_epoch_log(self, engine: Engine) -> None: def _default_iteration_log(self, engine: Engine) -> None: """ - Execute iteration log operation based on Ignite engine.state data. - The default behavior is to track loss from output[0] as output is a decollated list - and we replicated loss value for every item of the decollated list. + Execute iteration log operation based on Ignite `engine.state.output` data. + Log the values from `self.output_transform(engine.state.output)`. + Since `engine.state.output` is a decollated list and we replicated the loss value for every item + of the decollated list, the default behavior is to track the loss from `output[0]`. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. diff --git a/monai/handlers/stats_handler.py b/monai/handlers/stats_handler.py index c15abac542..b536ffaebb 100644 --- a/monai/handlers/stats_handler.py +++ b/monai/handlers/stats_handler.py @@ -108,7 +108,7 @@ def attach(self, engine: Engine) -> None: def epoch_completed(self, engine: Engine) -> None: """ Handler for train or validation/evaluation epoch completed Event. - Print epoch level log, default values are from Ignite state.metrics dict. + Print epoch level log, default values are from Ignite `engine.state.metrics` dict. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. @@ -122,7 +122,7 @@ def epoch_completed(self, engine: Engine) -> None: def iteration_completed(self, engine: Engine) -> None: """ Handler for train or validation/evaluation iteration completed Event. - Print iteration level log, default values are from Ignite state.logs dict. + Print iteration level log, default values are from Ignite `engine.state.output`. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. @@ -149,8 +149,8 @@ def exception_raised(self, engine: Engine, e: Exception) -> None: def _default_epoch_print(self, engine: Engine) -> None: """ - Execute epoch level log operation based on Ignite engine.state data. - print the values from Ignite state.metrics dict. + Execute epoch level log operation. + Default to print the values from Ignite `engine.state.metrics` dict. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. @@ -179,10 +179,10 @@ def _default_epoch_print(self, engine: Engine) -> None: def _default_iteration_print(self, engine: Engine) -> None: """ - Execute iteration log operation based on Ignite engine.state data. - Print the values from Ignite state.logs dict. - The default behavior is to print loss from output[0] as output is a decollated list and we replicated loss - value for every item of the decollated list. + Execute iteration log operation based on Ignite `engine.state.output` data. + Print the values from `self.output_transform(engine.state.output)`. + Since `engine.state.output` is a decollated list and we replicated the loss value for every item + of the decollated list, the default behavior is to print the loss from `output[0]`. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. diff --git a/monai/handlers/tensorboard_handlers.py b/monai/handlers/tensorboard_handlers.py index a3a0bf76b8..9d23662ba1 100644 --- a/monai/handlers/tensorboard_handlers.py +++ b/monai/handlers/tensorboard_handlers.py @@ -136,7 +136,7 @@ def attach(self, engine: Engine) -> None: def epoch_completed(self, engine: Engine) -> None: """ Handler for train or validation/evaluation epoch completed Event. - Write epoch level events, default values are from Ignite state.metrics dict. + Write epoch level events, default values are from Ignite `engine.state.metrics` dict. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. @@ -150,7 +150,7 @@ def epoch_completed(self, engine: Engine) -> None: def iteration_completed(self, engine: Engine) -> None: """ Handler for train or validation/evaluation iteration completed Event. - Write iteration level events, default values are from Ignite state.logs dict. + Write iteration level events, default values are from Ignite `engine.state.output`. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. @@ -163,8 +163,8 @@ def iteration_completed(self, engine: Engine) -> None: def _default_epoch_writer(self, engine: Engine, writer: SummaryWriter) -> None: """ - Execute epoch level event write operation based on Ignite engine.state data. - Default is to write the values from Ignite state.metrics dict. + Execute epoch level event write operation. + Default to write the values from Ignite `engine.state.metrics` dict. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. @@ -179,9 +179,10 @@ def _default_epoch_writer(self, engine: Engine, writer: SummaryWriter) -> None: def _default_iteration_writer(self, engine: Engine, writer: SummaryWriter) -> None: """ - Execute iteration level event write operation based on Ignite engine.state data. - The default behavior is to print loss from output[0] as output is a decollated list and we replicated loss - value for every item of the decollated list. + Execute iteration level event write operation based on Ignite `engine.state.output` data. + Extract the values from `self.output_transform(engine.state.output)`. + Since `engine.state.output` is a decollated list and we replicated the loss value for every item + of the decollated list, the default behavior is to track the loss from `output[0]`. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator.