torch_geometric.nn.aggr.DeepSetsAggregation

class DeepSetsAggregation(local_nn: Optional[Module] = None, global_nn: Optional[Module] = None)

Bases: Aggregation

执行深度集合聚合，其中要聚合的元素首先通过多层感知机（MLP）\(\phi_{\mathbf{\Theta}}\)进行转换，求和，然后通过另一个MLP \(\rho_{\mathbf{\Theta}}\)进行转换，如“具有自适应读数的图神经网络”论文中所建议的。

Parameters:

• local_nn (torch.nn.Module, optional) – 神经网络 \(\phi_{\mathbf{\Theta}}\), 例如, 由 torch.nn.Sequential 或 torch_geometric.nn.models.MLP 定义. (默认: None)

• global_nn (torch.nn.Module, optional) – 神经网络 \(\rho_{\mathbf{\Theta}}\), 例如, 由 torch.nn.Sequential 或 torch_geometric.nn.models.MLP 定义. (默认: None)

reset_parameters()

重置模块的所有可学习参数。

forward(x: Tensor, index: Optional[Tensor] = None, ptr: Optional[Tensor] = None, dim_size: Optional[int] = None, dim: int = -2) → Tensor

前向传播。

Parameters:

• x (torch.Tensor) – The source tensor.

• index (torch.Tensor, optional) – The indices of elements for applying the aggregation. One of index or ptr must be defined. (default: None)

• ptr (torch.Tensor, optional) – If given, computes the aggregation based on sorted inputs in CSR representation. One of index or ptr 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:

Tensor