def update(self, preds: torch.Tensor, target: torch.Tensor):
preds, target = _input_format_classification_one_hot(
self.num_classes, preds, target, self.threshold, self.multilabel)
# multiply because we are counting (1, 1) pair for true positives
self.true_positives += torch.sum(preds * target, dim=1)
self.predicted_positives += torch.sum(preds, dim=1)
def _fbeta_update(
preds: torch.Tensor,
target: torch.Tensor,
num_classes: int,
threshold: float = 0.5,
multilabel: bool = False
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
preds, target = _input_format_classification_one_hot(num_classes, preds, target, threshold, multilabel)
true_positives = torch.sum(preds * target, dim=1)
predicted_positives = torch.sum(preds, dim=1)
actual_positives = torch.sum(target, dim=1)
return true_positives, predicted_positives, actual_positives
def update(self, preds: torch.Tensor, target: torch.Tensor):
"""
Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
preds, target = _input_format_classification_one_hot(
self.num_classes, preds, target, self.threshold, self.multilabel)
# multiply because we are counting (1, 1) pair for true positives
self.true_positives += torch.sum(preds * target, dim=1)
self.actual_positives += torch.sum(target, dim=1)
