CombinedConstraint

class ptyrad.constraints.CombinedConstraint(constraint_params, device='cuda', verbose=True)

Bases: Module

Applies iteration-wise in-place constraints on optimizable tensors.

This class is designed to apply various constraints to a model’s parameters during the optimization process. The constraints are applied at specific iteration frequencies, as determined by the constraint_params dictionary. These constraints include orthogonality, probe amplitude constraints in Fourier space, intensity constraints, Gaussian blurring, Fourier filtering, and more.

Parameters:

• constraint_params (dict) – A dictionary containing the configuration for each constraint. Each constraint should have a frequency and other parameters necessary for its application.

• device (str, optional) – The device on which the tensors are located (e.g., ‘cuda’ or ‘cpu’). Defaults to ‘cuda’.

• verbose (bool, optional) – If True, prints messages during the application of constraints. Defaults to True.

__init__(constraint_params, device='cuda', verbose=True)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(constraint_params[, device, verbose])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
apply_complex_ratio(model, niter)	Apply complex constraint on object
apply_fix_probe_int(model, niter)	Apply probe intensity constraint
apply_kr_filter(model, niter)	Apply kr Fourier filter constraint on object
apply_kz_filter(model, niter)	Apply kz Fourier filter constraint on object
apply_mirrored_amp(model, niter)	Apply mirrored amplitude constraint on obja at voxel level
apply_obj_rblur(model, niter)	Apply Gaussian blur to object, this only applies to the last 2 dimension (...,H,W)
apply_obj_z_recenter(model, niter)	Apply object z-recentering along depth dimension
apply_obj_zblur(model, niter)	Apply Gaussian blur to object, this only applies to the last dimension (...,L)
apply_obja_thresh(model, niter)	Apply thresholding on obja at voxel level
apply_objp_postiv(model, niter)	Apply positivity constraint on objp at voxel level
apply_ortho_pmode(model, niter)	Apply orthogonality constraint to probe modes
apply_pos_recenter(model, niter)	Apply position recentering constraint on probe positions
apply_probe_mask_k(model, niter)	Apply probe amplitude constraint in Fourier space
apply_tilt_smooth(model, niter)	Apply Gaussian blur to object tilts
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.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set 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(model, niter)	Define the computation performed at every call.
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.
half()	Casts all floating point parameters and buffers to half datatype.
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()	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)	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_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
dump_patches
training