NRMSE#
- class deepinv.loss.metric.NRMSE(method='l2', **kwargs)[source]#
Bases:
NMSENormalized Root Mean Squared Error metric.
Calculates
\[\operatorname{NRMSE}(\hat{x},x) = \frac{\|\hat{x}-x\|_2}{\|x\|_2} = \sqrt{\operatorname{NMSE}(\hat{x},x)},\]where \(\hat{x}=\inverse{y}\).
Note
By default, no reduction is performed in the batch dimension.
- Example:
>>> import torch >>> from deepinv.loss.metric import NRMSE >>> m = NRMSE() >>> x_net = x = torch.ones(3, 2, 8, 8) >>> m(x_net, x) tensor([0., 0., 0.])
- Parameters:
method (str) – normalisation method. Currently only supports
l2.complex_abs (bool) – perform complex magnitude before passing data to metric function.
reduction (str) – method used to reduce scores over the batch dimension.
norm_inputs (str) – optional input normalization.
center_crop (int, tuple[int], None) – optional spatial center crop.
