SumPooling¶
-
class
dgl.nn.pytorch.glob.
SumPooling
[source]¶ Bases:
torch.nn.modules.module.Module
Apply sum pooling over the nodes in a graph.
\[r^{(i)} = \sum_{k=1}^{N_i} x^{(i)}_k\]Notes
Input: Could be one graph, or a batch of graphs. If using a batch of graphs, make sure nodes in all graphs have the same feature size, and concatenate nodes’ feature together as the input.
Examples
The following example uses PyTorch backend.
>>> 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
Case 1: Input a single graph
>>> sumpool(g1, g1_node_feats) tensor([[2.2282, 1.8667, 2.4338, 1.7540, 1.4511]])
Case 2: Input a batch of graphs
Build a batch of DGL graphs and concatenate all graphs’ node features into one tensor.
>>> 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]¶ Compute sum pooling.
- Parameters
graph (DGLGraph) – a DGLGraph or a batch of 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
The output feature with shape \((B, D)\), where \(B\) refers to the batch size of input graphs.
- Return type
torch.Tensor
-