SupLoss

class deepinv.loss.SupLoss(metric: Metric | Module = MSELoss())[source]

Bases: Loss

Standard supervised loss

The supervised loss is defined as

\[\|x-\inverse{y}\|^2\]

where \(\inverse{y}\) is the reconstructed signal and \(x\) is the ground truth target.

By default, the error is computed using the MSE metric, however any other metric (e.g., \(\ell_1\)) can be used as well. If called with arguments x_net, x, this is simply a wrapper for the metric metric.

Parameters:: metric (Metric, torch.nn.Module) – metric used for computing data consistency, which is set as the mean squared error by default.

forward(x_net, x, **kwargs)[source]