MOILoss

class deepinv.loss.MOILoss(physics: list[Physics] | Physics | None = None, physics_generator: PhysicsGenerator | None = None, metric: Metric | Module = MSELoss(), apply_noise=True, weight=1.0, rng=None)[source]

Bases: Loss

Multi-operator imaging loss

This loss can be used to learn when signals are observed via multiple (possibly incomplete) forward operators \(\{A_g\}_{g=1}^{G}\), i.e., \(y_i = A_{g_i}x_i\) where \(g_i\in \{1,\dots,G\}\) (see https://arxiv.org/abs/2201.12151).

The measurement consistency loss is defined as

\[\| \hat{x} - \inverse{A_g\hat{x},A_g} \|^2\]

where \(\hat{x}=\inverse{y,A_s}\) is a reconstructed signal (observed via operator \(A_s\)) and \(A_g\) is a forward operator sampled at random from a set \(\{A_g\}_{g=1}^{G}\).

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

The operators can be passed as a list of physics or as a single physics with a random physics generator.

Parameters:

  • physics (list[Physics], Physics) – list of physics containing the \(G\) different forward operators associated with the measurements, or single physics, or None. If single physics or None, physics generator must be used. If None, physics taken during forward.

  • physics_generator (PhysicsGenerator) – random physics generator that generates new params, if physics is not a list.

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

  • weight (float) – total weight of the loss

  • apply_noise (bool) – if True, the augmented measurement is computed with the full sensing model \(\sensor{\noise{\forw{\hat{x}}}}\) (i.e., noise and sensor model), otherwise is generated as \(\forw{\hat{x}}\).

  • rng (torch.Generator) – torch randon number generator for randomly selecting from physics list. If using physics generator, rng is ignored.

forward(x_net, physics, model, **kwargs)[source]

Computes the MOI loss.

Parameters:
Returns:

(torch.Tensor) loss.

next_physics(physics)[source]

Create random physics.

If physics is a list, select one at random. If physics generator is to be used, generate a new set of params at random.

Parameters:

physics (Physics) – forward physics. If None, use physics passed at init.

Examples using MOILoss:

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised learning from incomplete measurements of multiple operators.