SumPooling

class dgl.nn.pytorch.glob.SumPooling[source]

Bases: Module

在图中的节点上应用求和池化。

\[r^{(i)} = \sum_{k=1}^{N_i} x^{(i)}_k\]

注释

输入：可以是一个图，或者一批图。如果使用一批图，请确保所有图中的节点具有相同的特征大小，并将节点的特征连接在一起作为输入。

示例

以下示例使用PyTorch后端。

>>> import dgl
>>> import torch as th
>>> from dgl.nn import SumPooling
>>>
>>> g1 = dgl.rand_graph(3, 4)  # g1 is a random graph with 3 nodes and 4 edges
>>> g1_node_feats = th.rand(3, 5)  # feature size is 5
>>> g1_node_feats
tensor([[0.8948, 0.0699, 0.9137, 0.7567, 0.3637],
        [0.8137, 0.8938, 0.8377, 0.4249, 0.6118],
        [0.5197, 0.9030, 0.6825, 0.5725, 0.4755]])
>>>
>>> g2 = dgl.rand_graph(4, 6)  # g2 is a random graph with 4 nodes and 6 edges
>>> g2_node_feats = th.rand(4, 5)  # feature size is 5
>>> g2_node_feats
tensor([[0.2053, 0.2426, 0.4111, 0.9028, 0.5658],
        [0.5278, 0.6365, 0.9990, 0.2351, 0.8945],
        [0.3134, 0.0580, 0.4349, 0.7949, 0.3891],
        [0.0142, 0.2709, 0.3330, 0.8521, 0.6925]])
>>>
>>> sumpool = SumPooling()  # create a sum pooling layer

案例1：输入单个图形

>>> sumpool(g1, g1_node_feats)
tensor([[2.2282, 1.8667, 2.4338, 1.7540, 1.4511]])

案例2：输入一批图形

构建一批DGL图并将所有图的节点特征连接成一个张量。

>>> batch_g = dgl.batch([g1, g2])
>>> batch_f = th.cat([g1_node_feats, g2_node_feats])
>>>
>>> sumpool(batch_g, batch_f)
tensor([[2.2282, 1.8667, 2.4338, 1.7540, 1.4511],
        [1.0608, 1.2080, 2.1780, 2.7849, 2.5420]])

forward(graph, feat)[source]

计算求和池化。

Parameters:

graph (DGLGraph) – 一个DGLGraph或一批DGLGraphs
feat (torch.Tensor) – The input feature with shape \((N, D)\), where \(N\) is the number of nodes in the graph, and \(D\) means the size of features.

Returns:

输出特征的形状为\((B, D)\)，其中\(B\)表示输入图的批量大小。

Return type:

torch.Tensor