rbnet.base.Transition

class rbnet.base.Transition(*args, **kwargs)[source]

Bases: ABC, Module

Base class for RBN transitions, which have to implement inside_marginals().

Public Data Attributes:

dump_patches

training

call_super_init

forward(*input)

Define the computation performed at every call.
Inherited from Module

dump_patches

call_super_init

T_destination

training

Public Methods:

__init__(*args, **kwargs)

inside_marginals(location, inside_chart, ...)

Compute the marginals over inside probabilities
Inherited from Module

__init__(*args, **kwargs)

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

forward(*input)

Define the computation performed at every call.

register_buffer(name, tensor[, persistent])

Add a buffer to the module.

register_parameter(name, param)

Add a parameter to the module.

add_module(name, module)

Add a child module to the current module.

register_module(name, module)

Alias for add_module().

get_submodule(target)

Return the submodule given by target if it exists, otherwise throw an error.

set_submodule(target, module)

Set the submodule given by target if it exists, otherwise throw an error.

get_parameter(target)

Return the parameter given by target if it exists, otherwise throw an error.

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.

set_extra_state(state)

Set extra state contained in the loaded state_dict.

apply(fn)

Apply fn recursively to every submodule (as returned by .children()) as well as self.

cuda([device])

Move all model parameters and buffers to the GPU.

ipu([device])

Move all model parameters and buffers to the IPU.

xpu([device])

Move all model parameters and buffers to the XPU.

mtia([device])

Move all model parameters and buffers to the MTIA.

cpu()

Move all model parameters and buffers to the CPU.

type(dst_type)

Casts all parameters and buffers to dst_type.

float()

Casts all floating point parameters and buffers to float datatype.

double()

Casts all floating point parameters and buffers to double datatype.

half()

Casts all floating point parameters and buffers to half datatype.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

to_empty(*, device[, recurse])

Move the parameters and buffers to the specified device without copying storage.

to(*args, **kwargs)

Move and/or cast the parameters and buffers.

register_full_backward_pre_hook(hook[, prepend])

Register a backward pre-hook on the module.

register_backward_hook(hook)

Register a backward hook on the module.

register_full_backward_hook(hook[, prepend])

Register a backward hook on the module.

register_forward_pre_hook(hook, *[, ...])

Register a forward pre-hook on the module.

register_forward_hook(hook, *[, prepend, ...])

Register a forward hook on the module.

__call__(*args, **kwargs)

Call self as a function.

__getstate__()

__setstate__(state)

__getattr__(name)

__setattr__(name, value)

Implement setattr(self, name, value).

__delattr__(name)

Implement delattr(self, name).

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.

state_dict(*args[, destination, prefix, ...])

Return a dictionary containing references to the whole state of the module.

register_load_state_dict_pre_hook(hook)

Register a pre-hook to be run before module's load_state_dict() is called.

register_load_state_dict_post_hook(hook)

Register a post-hook to be run after module's load_state_dict() is called.

load_state_dict(state_dict[, strict, assign])

Copy parameters and buffers from state_dict into this module and its descendants.

parameters([recurse])

Return an iterator over module parameters.

named_parameters([prefix, recurse, ...])

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

buffers([recurse])

Return an iterator over module buffers.

named_buffers([prefix, recurse, ...])

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

children()

Return an iterator over immediate children modules.

named_children()

Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

modules()

Return an iterator over all modules in the network.

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.

train([mode])

Set the module in training mode.

eval()

Set the module in evaluation mode.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

zero_grad([set_to_none])

Reset gradients of all model parameters.

share_memory()

See torch.Tensor.share_memory_().

extra_repr()

Set the extra representation of the module.

__repr__()

Return repr(self).

__dir__()

Default dir() implementation.

compile(*args, **kwargs)

Compile this Module's forward using torch.compile().

Private Data Attributes:

_abc_impl

_version

This allows better BC support for load_state_dict().

_parameters

_buffers

_non_persistent_buffers_set

_backward_pre_hooks

_backward_hooks

_is_full_backward_hook

_forward_hooks

_forward_hooks_with_kwargs

_forward_hooks_always_called

_forward_pre_hooks

_forward_pre_hooks_with_kwargs

_state_dict_hooks

_load_state_dict_pre_hooks

_state_dict_pre_hooks

_load_state_dict_post_hooks

_modules

_compiled_call_impl

Inherited from ABC

_abc_impl

Inherited from Module

_version

This allows better BC support for load_state_dict().

_compiled_call_impl

_parameters

_buffers

_non_persistent_buffers_set

_backward_pre_hooks

_backward_hooks

_is_full_backward_hook

_forward_hooks

_forward_hooks_with_kwargs

_forward_hooks_always_called

_forward_pre_hooks

_forward_pre_hooks_with_kwargs

_state_dict_hooks

_load_state_dict_pre_hooks

_state_dict_pre_hooks

_load_state_dict_post_hooks

_modules

Private Methods:

Inherited from Module

_apply(fn[, recurse])

_get_backward_hooks()

Return the backward hooks for use in the call function.

_get_backward_pre_hooks()

_maybe_warn_non_full_backward_hook(inputs, ...)

_slow_forward(*input, **kwargs)

_wrapped_call_impl(*args, **kwargs)

_call_impl(*args, **kwargs)

_register_state_dict_hook(hook)

Register a post-hook for the state_dict() method.

_save_to_state_dict(destination, prefix, ...)

Save module state to the destination dictionary.

_register_load_state_dict_pre_hook(hook[, ...])

See register_load_state_dict_pre_hook() for details.

_load_from_state_dict(state_dict, prefix, ...)

Copy parameters and buffers from state_dict into only this module, but not its descendants.

_named_members(get_members_fn[, prefix, ...])

Help yield various names + members of modules.

_get_name()

_replicate_for_data_parallel()

abstract inside_marginals(location, inside_chart, terminal_chart, **kwargs)[source]

Compute the marginals over inside probabilities

for all possible splitting points (also see here). In particular, location specifies the variable’s location in the parse chart (the indices

and in the equation above), from which the possible splitting points follow ( splitting points for transitions of arity ). The marginals should always be returned in an array or iterable where the first dimension corresponds to all possible combinations of splitting points, even for transitions with arity (i.e. for , where there are no splits, the first dimension should be of size 1 and for all possible combinations of the splitting points should be listed in a flattened form in the first dimension). Additional dimensions, may be used to represent the dependency of the marginal on the variable (e.g. for a discrete variable, the second dimension may list the marginal for each possible value can take; and for a continuous variable, the marginal may be represented by a set of parameters).

The output of this function is typically handled by a custom implementation of Cell.inside_mixture().

Parameters:

  • location – location of the variable for which to compute the inside marginals

  • inside_chart – a lookup chart with inside probabilities for other variables

  • terminal_chart – a lookup chart with values of the terminal variables

Returns:

array-like or iterable with inside probabilities

training: bool