Loss

class deepinv.loss.Loss

Bases: Module

Base class for all loss functions.

Sets a template for the loss functions, whose forward method must follow the input parameters in deepinv.loss.Loss.forward().

adapt_model(model: Module, **kwargs) → Module

Some loss functions require the model forward call to be adapted before the forward pass.

Parameters:

model (torch.nn.Module) – reconstruction model

forward(x_net: Tensor, x: Tensor, y: Tensor, physics: Physics, model: Module, **kwargs) → Tensor

Computes the loss.

Parameters:

• x_net (torch.Tensor) – Reconstructed image \(\inverse{y}\).

• x (torch.Tensor) – Reference image.

• y (torch.Tensor) – Measurement.

• physics (deepinv.physics.Physics) – Forward operator associated with the measurements.

• model (torch.nn.Module) – Reconstruction function.

Returns:

(torch.Tensor) loss, the tensor size might be (1,) or (batch size,).

Examples using Loss:

Image transforms for equivariance & augmentations

Image transforms for equivariance & augmentations

Training a reconstruction network.

Training a reconstruction network.

Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing

Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing

Vanilla Unfolded algorithm for super-resolution

Vanilla Unfolded algorithm for super-resolution

Learned iterative custom prior

Learned iterative custom prior

Deep Equilibrium (DEQ) algorithms for image deblurring

Deep Equilibrium (DEQ) algorithms for image deblurring

Learned Primal-Dual algorithm for CT scan.

Learned Primal-Dual algorithm for CT scan.

Unfolded Chambolle-Pock for constrained image inpainting

Unfolded Chambolle-Pock for constrained image inpainting

Image transformations for Equivariant Imaging

Image transformations for Equivariant Imaging

Self-supervised learning with measurement splitting

Self-supervised learning with measurement splitting

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised denoising with the UNSURE loss.

Self-supervised denoising with the UNSURE loss.

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised learning from incomplete measurements of multiple operators.

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks