MeanIntersectionOverUnion#

class torch_uncertainty.metrics.segmentation.MeanIntersectionOverUnion(num_classes, top_k=1, ignore_index=None, validate_args=True, **kwargs)[source]#

Computes Mean Intersection over Union (IoU) score.

Parameters:

  • num_classes (int) – Integer specifying the number of classes.

  • top_k (int, optional) – Number of highest probability or logit score predictions considered to find the correct label. Only works when preds contain probabilities/logits. Defaults to 1.

  • ignore_index (int | None, optional) – Specifies a target value that is ignored and does not contribute to the metric calculation. Defaults to None.

  • validate_args (bool, optional) – Bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations. Defaults to True.

  • **kwargs – kwargs: Additional keyword arguments, see Advanced metric settings for more info.

Shape:

As input to forward and update the metric accepts the following input:

  • preds (Tensor): An int tensor of shape (B, *) or float tensor of shape (B, C, *). If preds is a floating point we apply torch.argmax along the C dimension to automatically convert probabilities/logits into an int tensor.

  • target (Tensor): An int tensor of shape (B, *).

As output to forward and compute the metric returns the following output:

  • mean_iou (Tensor): The computed Mean Intersection over Union (IoU) score. A tensor containing a single float value.

compute()[source]#

Compute the Means Intersection over Union (MIoU) based on saved inputs.