LION.operators.pcm.WalshHadamard2D

class LION.operators.pcm.WalshHadamard2D(height, width, device=None)

Bases: Operator

Walsh-Hadamard Transform operator.

Parameters

heightint

Height of the 2D input vector. Must be a power of two.

widthint

Width of the 2D input vector. Must be a power of two.

devicestr or torch.device

Device where tensors are placed.

Walsh-Hadamard Transform operator.

Parameters

heightint

Height of the 2D input vector. Must be a power of two.

widthint

Width of the 2D input vector. Must be a power of two.

devicestr or torch.device

Device where tensors are placed.

__init__(height, width, device=None)

Walsh-Hadamard Transform operator.

Parameters

heightint

Height of the 2D input vector. Must be a power of two.

widthint

Width of the 2D input vector. Must be a power of two.

devicestr or torch.device

Device where tensors are placed.

Methods

__init__(height, width[, device])	Walsh-Hadamard Transform operator.
add_module(name, module)	Add a child module to the current module.
adjoint(y)	Apply the adjoint of the Walsh-Hadamard Transform.
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 Walsh-Hadamard Transform.
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)	Apply the pseudo-inverse of the Walsh-Hadamard Transform.
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.
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 input domain.
dump_patches
range_shape	Return the shape of the output range.
training

forward(x)

Apply the Walsh-Hadamard Transform.

Note

Prefer calling the instance of the WalshHadamard2D operator as operator(x) over directly calling this method. See this PyTorch discussion.

adjoint(y)

Apply the adjoint of the Walsh-Hadamard Transform.

Parameters

ytorch.Tensor

Input vector of shape (N,).

Returns

torch.Tensor

Adjoint transformed vector of shape (height, width).

inverse(y)

Apply the pseudo-inverse of the Walsh-Hadamard Transform.

Parameters

ytorch.Tensor

Input vector of shape (N,).

Returns

torch.Tensor

Pseudo-inverse transformed vector of shape (N,).

property domain_shape

Return the shape of the input domain.

property range_shape

Return the shape of the output range.