MeanGTRelativeAbsoluteError

class torch_uncertainty.metrics.regression.MeanGTRelativeAbsoluteError(**kwargs)

Compute Mean Absolute Error relative to the Ground Truth (MAErel

or ARErel).

\[\text{MAErel} = \frac{1}{N}\sum_i^N \frac{| y_i - \hat{y_i} |}{y_i}\]

where \(y\) is a tensor of target values, and \(\hat{y}\) is a

tensor of predictions.

As input to forward and update the metric accepts the following

input:

• preds (Tensor): Predictions from model

• target (Tensor): Ground truth values

As output of forward and compute the metric returns the

following output:

• rel_mean_absolute_error (Tensor): A tensor with

  the relative mean absolute error over the state

Parameters:

kwargs – Additional keyword arguments, see Advanced metric settings.

Reference:

As in e.g. From big to small: Multi-scale local planar guidance for monocular depth estimation

update(pred, target)

Update state with predictions and targets.