pytorch_pfn_extras.training.extensions.evaluator.IgniteEvaluator

class pytorch_pfn_extras.training.extensions.evaluator.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.
set_evaluator_handlers()
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