MeanGTRelativeSquaredError#

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

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

This metric is useful for evaluating the relative squared error between predictions and targets, particularly in regression tasks where relative accuracy is critical.

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

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:

[1] From big to small: Multi-scale local planar guidance for monocular depth estimation.

Example:

from torch_uncertainty.metrics.regression import MeanGTRelativeSquaredError
import torch

# Initialize the metric
mse_rel_metric = MeanGTRelativeSquaredError(squared=True)

# Example predictions and targets
preds = torch.tensor([2.5, 1.0, 2.0, 8.0])
target = torch.tensor([3.0, 1.5, 2.0, 7.0])

# Update the metric state
mse_rel_metric.update(preds, target)

# Compute the Relative Mean Squared Error
result = mse_rel_metric.compute()
print(f"Relative Mean Squared Error: {result.item()}")
# Output: 0.09821434319019318

See also

update(pred, target)[source]#

Update state with predictions and targets.