# Set2Set¶

class dgl.nn.pytorch.glob.Set2Set(input_dim, n_iters, n_layers)[source]

Bases: torch.nn.modules.module.Module

Set2Set operator from Order Matters: Sequence to sequence for sets

For each individual graph in the batch, set2set computes

\begin{align}\begin{aligned}q_t &= \mathrm{LSTM} (q^*_{t-1})\\\alpha_{i,t} &= \mathrm{softmax}(x_i \cdot q_t)\\r_t &= \sum_{i=1}^N \alpha_{i,t} x_i\\q^*_t &= q_t \Vert r_t\end{aligned}\end{align}

for this graph.

Parameters
• input_dim (int) – The size of each input sample.

• n_iters (int) – The number of iterations.

• n_layers (int) – The number of recurrent layers.

Examples

The following example uses PyTorch backend.

>>> import dgl
>>> import torch as th
>>> from dgl.nn import Set2Set
>>>
>>> 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]])
>>>
>>> s2s = Set2Set(5, 2, 1)  # create a Set2Set layer(n_iters=2, n_layers=1)


Case 1: Input a single graph

>>> s2s(g1, g1_node_feats)
tensor([[-0.0235, -0.2291,  0.2654,  0.0376,  0.1349,  0.7560,  0.5822,  0.8199,


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)
>>>
>>> s2s(batch_g, batch_f)
tensor([[-0.0235, -0.2291,  0.2654,  0.0376,  0.1349,  0.7560,  0.5822,  0.8199,
0.5960,  0.4760],
[-0.0483, -0.2010,  0.2324,  0.0145,  0.1361,  0.2703,  0.3078,  0.5529,


Notes

Set2Set is widely used in molecular property predictions, see dgl-lifesci’s MPNN example on how to use DGL’s Set2Set layer in graph property prediction applications.

forward(graph, feat)[source]

Compute set2set pooling.

Parameters
• graph (DGLGraph) – The input graph.

• 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, and $$D$$ means the size of features.

Return type

torch.Tensor

reset_parameters()[source]

Reinitialize learnable parameters.