Shortcuts

Source code for torch_uncertainty.metrics.regression.mse_log

from torch import Tensor
from torchmetrics import MeanSquaredError


[docs]class MeanSquaredLogError(MeanSquaredError): def __init__(self, squared: bool = True, **kwargs) -> None: r"""MeanSquaredLogError (MSELog) regression metric. .. math:: \text{MSELog} = \frac{1}{N}\sum_i^N (\log \hat{y_i} - \log y_i)^2 where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predictions from model - ``target`` (:class:`~torch.Tensor`): Ground truth values As output of ``forward`` and ``compute`` the metric returns the following output: - ``mse_log`` (:class:`~torch.Tensor`): A tensor with the relative mean absolute error over the state Args: squared: If True returns MSELog value, if False returns EMSELog value. kwargs: Additional keyword arguments, see `Advanced metric settings <https://torchmetrics.readthedocs.io/en/stable/pages/overview.html#metric-kwargs>`_. Reference: As in e.g. From big to small: Multi-scale local planar guidance for monocular depth estimation """ super().__init__(squared, **kwargs)
[docs] def update(self, pred: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" return super().update(pred.log(), target.log())