meddlr.modeling.meta_arch.GeneralizedUnrolledCNN

class meddlr.modeling.meta_arch.GeneralizedUnrolledCNN(blocks: Union[Module, Sequence[Module]], step_sizes: Union[float, Sequence[float]] = -2.0, fix_step_size: bool = False, num_emaps: int = 1, vis_period: int = -1, num_grad_steps: int = None, order: Tuple[str] = ('dc', 'reg'))

Unrolled compressed sensing model.

This implementation is adapted from: MRSRL/dl-cs

Reference:

CM Sandino, JY Cheng, et al. “Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks” IEEE Signal Processing Magazine, 2020.

__init__(blocks: Union[Module, Sequence[Module]], step_sizes: Union[float, Sequence[float]] = -2.0, fix_step_size: bool = False, num_emaps: int = 1, vis_period: int = -1, num_grad_steps: int = None, order: Tuple[str] = ('dc', 'reg'))

Parameters

• blocks – A sequence of blocks

• step_sizes – Step size for data consistency prior to each block. If a single float is given, the same step size is used for all blocks.

• fix_step_size – Whether to fix the step size to a given value – i.e. set to True to make the step size non-trainable.

• num_emaps – Number of sensitivity maps used to estimate the image.

• vis_period – Number of steps between logging visualizations.

• num_grad_steps – Number of unrolled steps in the network. This is deprecated - the number of steps will be determined from the length of blocks.

• order – The order to apply the data consistency (dc) and model-based regularization (reg) blocks. One of ('dc', 'reg') or ('reg', 'dc').

Methods

__init__(blocks[, step_sizes, ...])	param blocks A sequence of blocks
add_module(name, module)	Adds a child module to the current module.
apply(fn)	Applies 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([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
dc(*, image, A, zf_image, step_size)
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Sets the module in evaluation mode.
extra_repr()	Set the extra representation of the module
float()	Casts all floating point parameters and buffers to float datatype.
forward(inputs[, return_pp, vis_training])	Reconstructs the image from the kspace.
from_config(cfg, **kwargs)	Build :cls:`GeneralizedUnrolledCNN` from a config.
get_buffer(target)	Returns the buffer given by target if it exists, otherwise throws an error.
get_extra_state()	Returns any extra state to include in the module's state_dict.
get_parameter(target)	Returns the parameter given by target if it exists, otherwise throws an error.
get_submodule(target)	Returns the submodule given by target if it exists, otherwise throws an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Moves all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse, ...])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns 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])	Returns 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, ...])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Returns an iterator over module parameters.
reg(*, image, model, dims)
register_backward_hook(hook)	Registers a backward hook on the module.
register_buffer(name, tensor[, persistent])	Adds a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Registers a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Registers a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Registers a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Registers a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Registers a post hook to be run after module's load_state_dict is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Adds a parameter to the module.
register_state_dict_pre_hook(hook)	These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	This function is called from load_state_dict() to handle any extra state found within the state_dict.
share_memory()	See torch.Tensor.share_memory_()
state_dict(*args[, destination, prefix, ...])	Returns a dictionary containing references to the whole state of the module.
step(*, image, model, A, zf_image, ...)
to(*args, **kwargs)	Moves and/or casts the parameters and buffers.
to_empty(*, device)	Moves the parameters and buffers to the specified device without copying storage.
train([mode])	Sets the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
visualize_training(kspace, zfs, targets, preds)	Visualize kspace data and reconstructions.
xpu([device])	Moves all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Sets gradients of all model parameters to zero.

Attributes

T_destination	alias of TypeVar('T_destination', bound=Dict[str, Any])
call_super_init
dump_patches