tfep.nn.embeddings.mafembed.PeriodicEmbedding

class tfep.nn.embeddings.mafembed.PeriodicEmbedding(n_features_in: int, limits: Sequence[float], periodic_indices: Sequence[int] | None = None)

Bases: MAFEmbedding

Lift periodic degrees of freedom into a periodic representation (cos, sin).

__init__(n_features_in: int, limits: Sequence[float], periodic_indices: Sequence[int] | None = None)

Constructor.

Parameters:

• n_features_in (int) – Number of input features.

• limits (Sequence[float]) – A pair (lower, upper) defining the limits of the periodic variables. The period is given by upper - lower.

• periodic_indices (Sequence[int] or None, optional) – Shape (n_periodic,). The (ordered) indices of the input features that are periodic and must be lifted to the (cos, sin) representation. If None, all features are embedded.

Methods

__init__(n_features_in, limits[, ...])	Constructor.
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()	Set the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(x)	Lift each periodic degree of freedom x into a periodic representation (cosx, sinx).
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_degrees_out(degrees_in)	Return the degrees of the features after the forward pass.
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

forward(x: Tensor) → Tensor

Lift each periodic degree of freedom x into a periodic representation (cosx, sinx).

Parameters:

x (torch.Tensor) – Shape (batch_size, n_features). Input tensor.

Returns:

out – Shape (batch_size, n_features + n_periodic). The input with the periodic DOFs transformed. The cosx, sinx representation is placed contiguously where the original DOF was. E.g., if 2 is the first element in periodic_indices, then cos and sin will be placed at y[:, 2] and y[:, 3] respectively.

Return type:

torch.Tensor

get_degrees_out(degrees_in: Tensor) → Tensor

Return the degrees of the features after the forward pass.

These are the degrees that will be passed as input to the conditioner.

The periodic features after the forward are represented as 2 features (cosine and sine) that both are assigned the same degree as the input feature.

Parameters:

degrees_in (torch.Tensor) – The degrees of the input features.

Returns:

degrees_out – The degrees of the features after the forward pass.

Return type:

torch.Tensor