torch_geometric.nn.aggr.DeepSetsAggregation
- class DeepSetsAggregation(local_nn: Optional[Module] = None, global_nn: Optional[Module] = None)[source]
Bases:
Aggregation
Performs Deep Sets aggregation in which the elements to aggregate are first transformed by a Multi-Layer Perceptron (MLP) \(\phi_{\mathbf{\Theta}}\), summed, and then transformed by another MLP \(\rho_{\mathbf{\Theta}}\), as suggested in the “Graph Neural Networks with Adaptive Readouts” paper.
- Parameters:
local_nn (torch.nn.Module, optional) – The neural network \(\phi_{\mathbf{\Theta}}\), e.g., defined by
torch.nn.Sequential
ortorch_geometric.nn.models.MLP
. (default:None
)global_nn (torch.nn.Module, optional) – The neural network \(\rho_{\mathbf{\Theta}}\), e.g., defined by
torch.nn.Sequential
ortorch_geometric.nn.models.MLP
. (default:None
)
- forward(x: Tensor, index: Optional[Tensor] = None, ptr: Optional[Tensor] = None, dim_size: Optional[int] = None, dim: int = -2) Tensor [source]
Forward pass.
- Parameters:
x (torch.Tensor) – The source tensor.
index (torch.Tensor, optional) – The indices of elements for applying the aggregation. One of
index
orptr
must be defined. (default:None
)ptr (torch.Tensor, optional) – If given, computes the aggregation based on sorted inputs in CSR representation. One of
index
orptr
must be defined. (default:None
)dim_size (int, optional) – The size of the output tensor at dimension
dim
after aggregation. (default:None
)dim (int, optional) – The dimension in which to aggregate. (default:
-2
)max_num_elements – (int, optional): The maximum number of elements within a single aggregation group. (default:
None
)
- Return type: