bartorch.linop.Callback#

class bartorch.linop.Callback(oshape, ishape, forward, adjoint, normal=None)#

A linear operator from Python functions, applied through BART.

Each function receives a view of BART’s buffer, without a copy, and returns a tensor; every application crosses into Python.

Parameters:

oshape (tuple of int) – Codomain and domain shapes, C order.
ishape (tuple of int) – Codomain and domain shapes, C order.
forward (callable) – Maps from ishape to oshape and back.
adjoint (callable) – Maps from ishape to oshape and back.
normal (callable, optional) – adjoint(forward(x)) in one function, where a cheaper form exists; without one BART composes the two.

Methods

`A`(x, **kwargs)	`A x`, under `deepinv`'s name.
`A_adjoint`(y, **kwargs)	`A^H y`, under `deepinv`'s name, recorded for autograd.
`A_adjoint_A`(x, **kwargs)	`A^H A x`, under `deepinv`'s name, recorded for autograd.
`A_dagger`(y, **kwargs)	The pseudo-inverse, under `deepinv`'s name.
`__init__`(oshape, ishape, forward, adjoint[, ...])
`adjoint`(y[, out])	`A^H y`, without recording for autograd.
`cogram`()	`A A^H` as an operator.
`conj`()	`conj(A)`: conjugate the input, apply, conjugate the output.
`forward`(x[, out])	`A x`, without recording for autograd.
`gram`()	`A^H A` as an operator.
`normal`(x[, out])	`A^H A x`.
`opnorm`()	The spectral norm, by BART's power iteration on `A^H A`.
`pinv`(y[, damp])	`(A^H A + damp I)^-1 A^H y`, the damped least-squares solution.
`to_nonlinear`()	The same operator as a `NonlinearOperator`.

Attributes

`H`	`A^H`, from BART's own adjoint constructor.
`T`	`A^T`, the adjoint without the conjugation, as `conj(A).H`.
`codim_rank`	How many axes the codomain has.
`codim_shape`	The codomain, under pyxu's name for it; the same as `oshape`.
`codim_size`	How many elements the codomain holds.
`device`
`dim_rank`	How many axes the domain has.
`dim_shape`	The domain, under pyxu's name for it; the same as `ishape`.
`dim_size`	How many elements the domain holds.
`ishape`
`oshape`

bartorch.linop.Callback