MeanGTRelativeSquaredError

class torch_uncertainty.metrics.regression.MeanGTRelativeSquaredError(squared=True, num_outputs=1, **kwargs)

Compute mean squared error relative to the Ground Truth (MSErel or SRE).

\[\text{MSErel} = \frac{1}{N}\sum_i^N \frac{(y_i - \hat{y_i})^2}{y_i}\]

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_squared_error (Tensor): A tensor with

  the relative mean squared error

Parameters:

• squared – If True returns MSErel value, if False returns RMSErel value.

• num_outputs – Number of outputs in multioutput setting

• 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.