gd_dl.models.Weight_CGConv

class gd_dl.models.Weight_CGConv(channels: int | Tuple[int, int], dim: int = 0, aggr: str = 'add', batch_norm: bool = False, bias: bool = True, **kwargs)

The crystal graph convolutional operator from the “Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties” paper We modifed the crystal graph convolutional operator to incorporate manual edge weights

__init__(channels: int | Tuple[int, int], dim: int = 0, aggr: str = 'add', batch_norm: bool = False, bias: bool = True, **kwargs)

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

Methods

__init__(channels[, dim, aggr, batch_norm, bias])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
aggregate(inputs, index[, ptr, dim_size])	Aggregates messages from neighbors as \(\bigoplus_{j \in \mathcal{N}(i)}\).
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.
edge_update()	Computes or updates features for each edge in the graph.
edge_updater(edge_index[, size])	The initial call to compute or update features for each edge in the graph.
eval()	Set the module in evaluation mode.
explain_message(inputs, dim_size)
extra_repr()	Set the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(x, edge_index[, edge_attr, edge_weight])	Runs the forward pass of the module.
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.
jittable([typing])	Analyzes the MessagePassing instance and produces a new jittable module that can be used in combination with torch.jit.script().
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
message(x_i, x_j, edge_attr, edge_weight)	Constructs messages from node \(j\) to node \(i\) in analogy to \(\phi_{\mathbf{\Theta}}\) for each edge in edge_index.
message_and_aggregate(edge_index)	Fuses computations of message() and aggregate() into a single function.
modules()	Return an iterator over all modules in the network.
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.
propagate(edge_index[, size])	The initial call to start propagating messages.
register_aggregate_forward_hook(hook)	Registers a forward hook on the module.
register_aggregate_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_edge_update_forward_hook(hook)	Registers a forward hook on the module.
register_edge_update_forward_pre_hook(hook)	Registers a forward pre-hook on 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_message_and_aggregate_forward_hook(hook)	Registers a forward hook on the module.
register_message_and_aggregate_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_message_forward_hook(hook)	Registers a forward hook on the module.
register_message_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_propagate_forward_hook(hook)	Registers a forward hook on the module.
register_propagate_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
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.
reset_parameters()	Resets all learnable parameters of the module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
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(inputs)	Updates node embeddings in analogy to \(\gamma_{\mathbf{\Theta}}\) for each node \(i \in \mathcal{V}\).
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

SUPPORTS_FUSED_EDGE_INDEX
T_destination
call_super_init
decomposed_layers
dump_patches
explain
special_args
training