DeepInvPhyNufft

class mrinufft.operators.autodiff.DeepInvPhyNufft(autograd_nufft)

Bases: LinearPhysics

Expose an MRINufftAutoGrad as as DeepInv Physics Operator.

Methods

A	Forward operation.
A_A_adjoint	A helper function that computes \(A A^{\top}y\).
A_adjoint	Adjoint operation.
A_adjoint_A	A helper function that computes \(A^{\top}Ax\).
A_dagger	Adjoint operation.
A_vjp	Computes the product between a vector \(v\) and the Jacobian of the forward operator \(A\) evaluated at \(x\), defined as:
__init__	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add
add_module	Add a child module to the current module.
adjointness_test	Numerically check that \(A^{\top}\) is indeed the adjoint of \(A\).
apply	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers	Return an iterator over module buffers.
children	Return an iterator over immediate children modules.
clone	Clone the forward operator by performing deepcopy to copy all attributes to new memory.
compile	Compile this Module's forward using torch.compile().
compute_norm	Computes the spectral \(\ell_2\) norm (Lipschitz constant) of the operator \(A\).
compute_sqnorm	Computes the squared spectral \(\ell_2\) norm of the operator \(A\).
condition_number	Computes an approximation of the condition number of the linear operator \(A\).
cpu	Move all model parameters and buffers to the CPU.
cuda	Move all model parameters and buffers to the GPU.
double	Casts all floating point parameters and buffers to double datatype.
eval	Set the module in evaluation mode.
extra_repr	Return the extra representation of the module.
float	Casts all floating point parameters and buffers to float datatype.
forward	Computes forward operator
get_buffer	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state	Return any extra state to include in the module's state_dict.
get_parameter	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule	Return the submodule given by target if it exists, otherwise throw an error.
half	Casts all floating point parameters and buffers to half datatype.
ipu	Move all model parameters and buffers to the IPU.
load_state_dict	Copy parameters and buffers from state_dict into this module and its descendants.
modules	Return an iterator over all modules in the network.
mtia	Move all model parameters and buffers to the MTIA.
named_buffers	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
noise	Incorporates noise into the measurements \(\tilde{y} = N(y)\)
parameters	Return an iterator over module parameters.
prox_l2	Computes proximal operator of \(f(x) = \frac{1}{2}\|Ax-y\|^2\), i.e.,
register_backward_hook	Register a backward hook on the module.
register_buffer	Add a buffer to the module.
register_forward_hook	Register a forward hook on the module.
register_forward_pre_hook	Register a forward pre-hook on the module.
register_full_backward_hook	Register a backward hook on the module.
register_full_backward_pre_hook	Register a backward pre-hook on the module.
register_load_state_dict_post_hook	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook	Register a pre-hook to be run before module's load_state_dict() is called.
register_module	Alias for add_module().
register_parameter	Add a parameter to the module.
register_state_dict_post_hook	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook	Register a pre-hook for the state_dict() method.
requires_grad_	Change if autograd should record operations on parameters in this module.
sensor	Computes sensor non-linearities \(y = \eta(y)\)
set_extra_state	Set extra state contained in the loaded state_dict.
set_ls_solver	Change default solver for computing the least squares solution:
set_noise_model	Sets the noise model
set_submodule	Set the submodule given by target if it exists, otherwise throw an error.
share_memory	See torch.Tensor.share_memory_().
stack	Stacks forward operators \(A = \begin{bmatrix} A_1 \\ A_2 \end{bmatrix}\).
state_dict	Return a dictionary containing references to the whole state of the module.
to	Move and/or cast the parameters and buffers.
to_empty	Move the parameters and buffers to the specified device without copying storage.
train	Set the module in training mode.
type	Casts all parameters and buffers to dst_type.
update	Update the parameters of the physics: forward operator and noise model.
update_parameters	Update the parameters of the forward operator.
xpu	Move all model parameters and buffers to the XPU.
zero_grad	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
training

Examples using mrinufft.operators.autodiff.DeepInvPhyNufft

Model-based iterative reconstruction

Model-based iterative reconstruction

A(x: Tensor, **kwargs) → Tensor

Forward operation.

Return type:

Tensor

A_adjoint(y: Tensor, **kwargs) → Tensor

Adjoint operation.

Return type:

Tensor

A_dagger(y: Tensor, **kwargs) → Tensor

Adjoint operation.

Return type:

Tensor