magnet.aggmodels.rlpartitioner.DRLCPGatti#

class magnet.aggmodels.rlpartitioner.DRLCPGatti(hid_conv, hid_lin, input_features: int = 2)#

Bases: DRLCoarsePartioner

Deep Reinforcement Learning coarse partitioner by A. Gatti et al.

Graph Neural network with 4 GAT convolutional layers followed by 2 dense layers common to both actor and critic.

Constructor

__init__(hid_conv, hid_lin, input_features: int = 2)#

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

Methods

__init__(hid_conv, hid_lin[, input_features])

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

forward(graph)

Define the computation performed at every call.
__init__(hid_conv, hid_lin, input_features: int = 2)#

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

forward(graph)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Inherited Methods

A2C_train(training_dataset[, batch_size, ...])

ac_eval(graph[, perc])

agglomerate(mesh[, mode, nref, mult_factor])

Agglomerate a mesh.

agglomerate_dataset(dataset, **kwargs)

Agglomerate all meshes in a dataset.

bisect(mesh)

Bisect the mesh once.

bisection_Nref(mesh, Nref[, warm_start])

Bisect the mesh recursively a set number of times.

bisection_mult_factor(mesh, mult_factor[, ...])

Bisect a mesh until the agglomerated elements are small enough.

bisection_segregated(mesh, mult_factor[, subset])

Bisect heterogeneous mesh until elements are small enough.

change_vert(graph, action)

In place change of vertex to other subgraph.

coarsen(mesh, subset[, mode, nref, mult_factor])

Coarsen a subregion of the mesh.

compute_episode_length(graph)

cut(graph)

get_number_of_parameters()

Get total number of parameters of the GNN.

get_sample(mesh, **kwargs)

create a graph data structure sample from a mesh.

load_model(model_path)

Load model from state dictionary.

loss_function(output, graph)

Loss function used during training.

multi_eval(graph[, step, perc])

multilevel_bisection(mesh[, refiner, ...])

normalize(x)

Normalize the data before feeding it to the GNN.

reward_function(new_state, old_state, ...)

save_model(output_path)

Save current model to state dictionary.

train_GNN(training_dataset, ...[, ...])

Train the Graph Neural Network.

update_state(graph, action)

volumes(graph)