GlobalAttentionPooling

class dgl.nn.pytorch.glob.GlobalAttentionPooling(gate_nn, feat_nn=None)

Bases: torch.nn.modules.module.Module

Global Attention Pooling from Gated Graph Sequence Neural Networks

\[r^{(i)} = \sum_{k=1}^{N_i}\mathrm{softmax}\left(f_{gate} \left(x^{(i)}_k\right)\right) f_{feat}\left(x^{(i)}_k\right)\]

Parameters

• gate_nn (torch.nn.Module) – A neural network that computes attention scores for each feature.

• feat_nn (torch.nn.Module, optional) – A neural network applied to each feature before combining them with attention scores.

Examples

The following example uses PyTorch backend.

>>> import dgl
>>> import torch as th
>>> from dgl.nn import GlobalAttentionPooling
>>>
>>> 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]])
>>>
>>> gate_nn = th.nn.Linear(5, 1)  # the gate layer that maps node feature to scalar
>>> gap = GlobalAttentionPooling(gate_nn)  # create a Global Attention Pooling layer

Case 1: Input a single graph

>>> gap(g1, g1_node_feats)
tensor([[0.7410, 0.6032, 0.8111, 0.5942, 0.4762]],
       grad_fn=<SegmentReduceBackward>)

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], 0)
>>>
>>> gap(batch_g, batch_f)
tensor([[0.7410, 0.6032, 0.8111, 0.5942, 0.4762],
        [0.2417, 0.2743, 0.5054, 0.7356, 0.6146]],
       grad_fn=<SegmentReduceBackward>)

Notes

See our GGNN example on how to use GatedGraphConv and GlobalAttentionPooling layer to build a Graph Neural Networks that can solve Soduku.

forward(graph, feat, get_attention=False)

Compute global attention pooling.

Parameters

• graph (DGLGraph) – A DGLGraph or a batch of DGLGraphs.

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

• get_attention (bool, optional) – Whether to return the attention values from gate_nn. Default to False.

Returns

• torch.Tensor – The output feature with shape \((B, D)\), where \(B\) refers to the batch size.

• torch.Tensor, optional – The attention values of shape \((N, 1)\), where \(N\) is the number of nodes in the graph. This is returned only when get_attention is True.