spyrit.core.recon.PinvNet

class spyrit.core.recon.PinvNet(acqu: Linear, prep, denoi=Identity(), *, device: device = device(type='cpu'), **pinv_kwargs)

Bases: _PrebuiltFullNet

Pre-built FullNet that uses a pseudo inverse.

As a FullNet, this network has two modules: one for measurements and one for reconstruction.

The measurement module only contains the acquisition operator. The reconstruction module contains a preprocessing operator, a pseudo inverse operator, and a denoising operator.

The optional keyword arguments passed at initialization are fed in the pseudo inverse operator. This way, the regularization can be controlled directly from the PinvNet constructor.

Args:

acqu (spyrit.core.meas): Acquisition operator (see meas)

prep (spyrit.core.prep): Preprocessing operator (see prep)

denoi (torch.nn.Module, optional): Image denoising operator. Default is Identity.

**pinv_kwargs: Optional keyword arguments passed to the pseudo inverse operator (see PseudoInverse).

Attributes:

acqu (spyrit.core.meas): Acquisition operator.

acqu_modules (nn.Sequential): Measurement modules. Only contains the acquisition operator.

prep (spyrit.core.prep): Preprocessing operator.

inv (spyrit.core.inverse.PseudoInverse): Pseudo inverse operator.

denoi (torch.nn.Module): Image denoising operator.

recon_modules (nn.Sequential): Reconstruction modules. Contains the preprocessing operator, the pseudo inverse operator, and the denoising operator.

pinv_kwargs (dict): Optional keyword arguments passed to the pseudo inverse operator.

Input / Output:

input: Ground-truth images with shape \((b,c,h,w)\), with \(b\) being the batch size, \(c\) the number of channels, and \(h\) and \(w\) the height and width of the images.

output: Reconstructed images with shape \((b,c,h,w)\).

Example:

>>> import spyrit
>>> acqu = spyrit.core.meas.HadamSplit2d(32)
>>> prep = spyrit.core.prep.Rescale(1.0)
>>> pinv = PinvNet(acqu, prep, device=torch.device("cpu"))

Example with a regularized pseudo inverse:

>>> import spyrit
>>> noise_model = spyrit.core.noise.Poisson(100)
>>> acqu = spyrit.core.meas.HadamSplit2d(32, noise_model=noise_model)
>>> prep = spyrit.core.prep.Rescale(100)
>>> pinv = PinvNet(acqu, prep, use_fast_pinv=False, store_H_pinv=True, regularization='H1', eta=1e-6, img_shape=(32, 32))

Methods

acquire(x)	Apply the measurement modules to the input signal.
forward(x)	Apply the full network to the input signal.
reconstruct(y)	Apply the reconstruction modules to the input measurements.
reconstruct_pinv(y)	Reconstructs measurement vectors without denoising.