MCLoss#

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

Bases: Loss

Measurement consistency loss

This loss enforces that the reconstructions are measurement-consistent, i.e., \(y=\forw{\inverse{y}}\).

The measurement consistency loss is defined as

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

where \(\inverse{y}\) is the reconstructed signal and \(A\) is a forward operator.

By default, the error is computed using the MSE metric, however any other metric (e.g., \(\ell_1\)) can be used as well.

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

forward(y, x_net, physics, **kwargs)[source]#

Computes the measurement splitting loss

Parameters:

y (torch.Tensor) – measurements.
x_net (torch.Tensor) – reconstructed image \(\inverse{y}\).
physics (deepinv.physics.Physics) – forward operator associated with the measurements.

Returns:

(torch.Tensor) loss.

Examples using `MCLoss`:#