pytorch_pfn_extras.training.extensions.IgniteEvaluator#
- class pytorch_pfn_extras.training.extensions.IgniteEvaluator(evaluator, iterator, target, **kwargs)#
Bases:
Evaluator
Methods
__init__
(evaluator, iterator, target, **kwargs)add_metric
(metric_fn)Adds a custom metric to the evaluator.
eval_func
(*args, **kwargs)evaluate
()Evaluates the model and returns a result dictionary.
finalize
(manager)Finalizes the extension.
get_all_iterators
()Returns a dictionary of all iterators.
get_all_targets
()Returns a dictionary of all target links.
get_iterator
(name)Returns the iterator of the given name.
get_target
(name)Returns the target link of the given name.
initialize
(manager)Initializes up the manager state.
load_state_dict
(to_load)on_error
(manager, exc, tb)Handles the error raised during training before finalization.
state_dict
()Serializes the extension state.
Attributes
default_name
is_async
name
needs_model_state
priority
trigger
- Parameters:
evaluator (Engine) –
iterator (Union[DataLoader[Any], Dict[str, DataLoader[Any]]]) –
target (Union[Module, Dict[str, Module]]) –
kwargs (Any) –
- __init__(evaluator, iterator, target, **kwargs)#
- Parameters:
evaluator (Engine) –
iterator (Union[DataLoader[Any], Dict[str, DataLoader[Any]]]) –
target (Union[Module, Dict[str, Module]]) –
kwargs (Any) –
- evaluate()#
Evaluates the model and returns a result dictionary.
This method runs the evaluation loop over the validation dataset. It accumulates the reported values to
DictSummary
and returns a dictionary whose values are means computed by the summary.Users can override this method to customize the evaluation routine.
- Returns:
Result dictionary. This dictionary is further reported via
report()
without specifying any observer.- Return type:
dict
- set_evaluator_handlers()#
- Return type:
None