meddlr.modeling.meta_arch.CSModel#

class meddlr.modeling.meta_arch.CSModel(reg: float, max_iter: int, device='cpu', num_emaps: int = 1)[source]#

Compressed sensing reconstruction with l1 wavelet regularization.

This class is a PyTorch wrapper around the SigPy’s L1WaveletRecon class. On each forward pass, each example is reconstructed using \(\ell_1\) wavelet-regularized compressed sensing.

If the model should run on a GPU, cupy must be installed.

Note

Gradients are not supported.

Variables

  • device (torch.Device | str) – Device to use for execution.

  • l1_reg (float) – \(\ell_1\) regularization parameter.

  • max_iter (int) – Maximum number of iterations.

  • num_emaps (int) – Number of sensitivity maps.

__init__(reg: float, max_iter: int, device='cpu', num_emaps: int = 1)[source]#
Parameters

  • reg (float) – The regularization strength.

  • max_iter (int) – Maximum number of iterations.

  • device (str | torch.device, optional) – The device to execute on.

  • num_emaps (int, optional) – Number of estimated sensitivity maps. Currently only 1 is supported.

Methods

__init__(reg, max_iter[, device, num_emaps])

param reg

The regularization strength.

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.

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])

TODO: condense into list of dataset dicts. :param inputs: Standard ss_recon module input dictionary * "kspace": Kspace. If fully sampled, and want to simulate undersampled kspace, provide "mask" argument. * "maps": Sensitivity maps * "target" (optional): Target image (typically fully sampled). * "mask" (optional): Undersampling mask to apply. * "signal_model" (optional): The signal model. If provided, "maps" will not be used to estimate the signal model. Use with caution. :param return_pp: If True, return post-processing parameters "mean", "std", and "norm" if included in the input. :type return_pp: bool, optional :param vis_training: If True, force visualize training on this pass. Can only be True if model is in training mode. :type vis_training: bool, optional.

from_config(cfg)

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.

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.

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.

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