CWN

CWN class.

class topomodelx.nn.cell.cwn.CWN(in_channels_0, in_channels_1, in_channels_2, hid_channels, n_layers, **kwargs)

Implementation of a specific version of CW network [1].

Parameters:

in_channels_0int

Dimension of input features on nodes (0-cells).

in_channels_1int

Dimension of input features on edges (1-cells).

in_channels_2int

Dimension of input features on faces (2-cells).

hid_channelsint

Dimension of hidden features.

n_layersint

Number of CWN layers.

**kwargsoptional

Additional arguments CWNLayer.

Methods

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(x_0, x_1, x_2, adjacency_0, ...)	Forward computation through projection, convolutions, linear layers and average pooling.
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.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

__call__

References

[1]

Bodnar, et al. Weisfeiler and Lehman go cellular: CW networks. NeurIPS 2021. https://arxiv.org/abs/2106.12575

forward(x_0, x_1, x_2, adjacency_0, incidence_2, incidence_1_t)

Forward computation through projection, convolutions, linear layers and average pooling.

Parameters:

x_0torch.Tensor, shape = (n_nodes, in_channels_0)

Input features on the nodes (0-cells).

x_1torch.Tensor, shape = (n_edges, in_channels_1)

Input features on the edges (1-cells).

x_2torch.Tensor, shape = (n_faces, in_channels_2)

Input features on the faces (2-cells).

adjacency_0torch.Tensor, shape = (n_edges, n_edges)

Upper-adjacency matrix of rank 1.

incidence_2torch.Tensor, shape = (n_edges, n_faces)

Boundary matrix of rank 2.

incidence_1_ttorch.Tensor, shape = (n_edges, n_nodes)

Coboundary matrix of rank 1.

Returns:

x_0torch.Tensor, shape = (n_nodes, in_channels_0)

Final hidden states of the nodes (0-cells).

x_1torch.Tensor, shape = (n_edges, in_channels_1)

Final hidden states the edges (1-cells).

x_2torch.Tensor, shape = (n_edges, in_channels_2)

Final hidden states of the faces (2-cells).