SortPooling
- class dgl.nn.pytorch.glob.SortPooling(k)[source]
Bases:
Module排序池化来自图分类的端到端深度学习架构
它首先沿着特征维度按升序对节点特征进行排序，并选择前k个节点的排序特征（按每个节点的最大值排序）。
- Parameters:
k (int) – 每个图要保留的节点数。
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
>>> import dgl >>> import torch as th >>> from dgl.nn import SortPooling >>> >>> 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]]) >>> >>> sortpool = SortPooling(k=2) # create a sort pooling layer
案例1：输入单个图形
>>> sortpool(g1, g1_node_feats) tensor([[0.0699, 0.3637, 0.7567, 0.8948, 0.9137, 0.4755, 0.5197, 0.5725, 0.6825, 0.9030]])
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]) >>> >>> sortpool(batch_g, batch_f) tensor([[0.0699, 0.3637, 0.7567, 0.8948, 0.9137, 0.4755, 0.5197, 0.5725, 0.6825, 0.9030], [0.2351, 0.5278, 0.6365, 0.8945, 0.9990, 0.2053, 0.2426, 0.4111, 0.5658, 0.9028]])