LION.operators.pcm.PhotocurrentMapOp

class LION.operators.pcm.PhotocurrentMapOp(J, subsampler, wht_dim=-1, device=None)

Bases: Operator

Photocurrent mapping operator using subsampled WHT and dyadic permutation.

Parameters

Jint

The exponent such that the image size is (2^J, 2^J).

subsamplerSubsampler

The subsampler defining the measurement indices.

wht_dimint, optional

The dimension along which to apply the WHT. Default is -1 (last dimension).

devicestr or torch.device

Device where tensors are placed.

Initialize the PhotocurrentMapOp.

__init__(J, subsampler, wht_dim=-1, device=None)

Initialize the PhotocurrentMapOp.

Methods

__init__(J, subsampler[, wht_dim, device])	Initialize the PhotocurrentMapOp.
add_module(name, module)	Add a child module to the current module.
adjoint(y)	Apply the adjoint photocurrent mapping.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
autograd()	The operator and its adjoint as torch autograd functions of batches, (forward, adjoint): functions whose gradients torch knows (the gradient of A is A^T, and of A^T is A), for models and solvers whose gradients go through the operator (e.g. LPD, Sparse2Inverse).
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
default_reconstruction(y)	The operator's usual reconstruction of measurements y (default_reconstruction_fn, e.g. FDK for CT).
default_reconstruction_fn()	The operator's usual reconstruction, as a function f(y, op) of measurements and operator that a record can name (e.g. fdk for CT).
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(x)	Apply the forward photocurrent mapping.
get_buffer(target)	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(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
gram(x)	The Gram operator, AᵀA, applied to x.
half()	Casts all floating point parameters and buffers to half datatype.
inverse(y)	The inverse applied to y, for operators that have one (e.g. an orthogonal transform): a subclass defines it only then, and its default reconstruction is then the inverse.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules([remove_duplicate])	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	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([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
pseudo_inv(y)	Apply the pseudo-inverse photocurrent mapping.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_device(device)	Moves the operator and its sub-operators to device, updating their device attributes (which nn.Module.to doesn't).
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
domain_shape	Return the shape of the image domain.
dump_patches
range_shape	Return the shape of the measurement range.
training

forward(x)

Apply the forward photocurrent mapping.

Note

Prefer calling the instance of the PhotocurrentMapOp operator as operator(x) over directly calling this method. See this PyTorch discussion <https://discuss.pytorch.org/t/is-model-forward-x-the-same-as-model-call-x/33460/3>

adjoint(y)

Apply the adjoint photocurrent mapping.

Parameters

ytorch.Tensor

The input measurements to which the adjoint photocurrent mapping is applied.

Returns

torch.Tensor

The reconstructed image from the adjoint photocurrent mapping.

pseudo_inv(y)

Apply the pseudo-inverse photocurrent mapping.

Parameters

ytorch.Tensor

The input measurements to which the pseudo-inverse photocurrent mapping is applied.

Returns

torch.Tensor

The reconstructed image from the pseudo-inverse photocurrent mapping.

property domain_shape

Return the shape of the image domain.

property range_shape

Return the shape of the measurement range.