pydpf.filtering.StraightThroughDPF

class pydpf.filtering.StraightThroughDPF(SSM: FilteringModel = None, resampling_generator: Generator = <torch._C.Generator object>, multinomial: bool = False, *, use_REINFORCE_for_proposal: bool = False, use_REINFORCE_for_initial_proposal: bool = False)

Bases: ParticleFilter

Similar to the DPF but the gradient of the state is passed through resampling without modification. (T. Le et al. ‘Auto-encoding sequential monte carlo’ 2018, C. Maddison et al. ‘ Filtering variational objectives’ 2018, and C. Naesseth et al. ‘Variational sequential monte carlo’ 2018.) Equivelant to soft resampling with a softness parameter of 1.

Parameters:

SSM: FilteringModel

A FilteringModel that represents the SSM (and optionally a proposal model). See the documentation of FilteringModel for more complete information.

resampling_generator: torch.Generator

Generator to track the resampling rng.

multinomial: bool. Default False.

If true then use multinomial resampling. Otherwise, use systematic resampling.

use_REINFORCE_for_proposal: bool. Default False.

Whether to use the REINFORCE estimator for the gradient due to the particle proposal process.

use_REINFORCE_for_initial_proposal: bool. Default False.

Whether to use the REINFORCE estimator for the gradient due to the initial particle proposal process.

__init__(SSM: FilteringModel = None, resampling_generator: Generator = <torch._C.Generator object>, multinomial: bool = False, *, use_REINFORCE_for_proposal: bool = False, use_REINFORCE_for_initial_proposal: bool = False) → None

Parameters:

SSM: FilteringModel

A FilteringModel that represents the SSM (and optionally a proposal model). See the documentation of FilteringModel for more complete information. If this parameter is not None then the values of initial_proposal and proposal are ignored.

resampling_generator:

The generator to track the resampling rng.

Methods

__init__(SSM, resampling_generator, ...)
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.
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()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(n_particles, time_extent, ...[, ...])	Run a forward pass of the SIS filter.
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([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_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.
update()	Update all constrained_parameters and cached_properties belonging to this Module.
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