metric

allennlp.training.metrics.metric

[SOURCE]

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Metric

class Metric(Registrable)

A very general abstract class representing a metric which can be accumulated.

supports_distributed

class Metric(Registrable):
 | ...
 | supports_distributed = False

__call__

class Metric(Registrable):
 | ...
 | def __call__(
 |     self,
 |     predictions: torch.Tensor,
 |     gold_labels: torch.Tensor,
 |     mask: Optional[torch.BoolTensor]
 | )

Parameters

• predictions : torch.Tensor
  A tensor of predictions.
• gold_labels : torch.Tensor
  A tensor corresponding to some gold label to evaluate against.
• mask : torch.BoolTensor, optional (default = None)
  A mask can be passed, in order to deal with metrics which are computed over potentially padded elements, such as sequence labels.

get_metric

class Metric(Registrable):
 | ...
 | def get_metric(self, reset: bool) -> Dict[str, Any]

Compute and return the metric. Optionally also call self.reset.

reset

class Metric(Registrable):
 | ...
 | def reset(self) -> None

Reset any accumulators or internal state.

detach_tensors

class Metric(Registrable):
 | ...
 | @staticmethod
 | def detach_tensors(*tensors: torch.Tensor) -> Iterable[torch.Tensor]

If you actually passed gradient-tracking Tensors to a Metric, there will be a huge memory leak, because it will prevent garbage collection for the computation graph. This method ensures the tensors are detached.