WeightAndSum

class dgl.nn.pytorch.glob.WeightAndSum(in_feats)

Bases: Module

Compute importance weights for atoms and perform a weighted sum.

Parameters:

in_feats (int) – Input atom feature size

Examples

The following example uses PyTorch backend.

>>> import dgl
>>> import torch as th
>>> from dgl.nn import WeightAndSum
>>>
>>> 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]])
>>>
>>> weight_and_sum = WeightAndSum(5)  # create a weight and sum layer(in_feats=16)

Case 1: Input a single graph

>>> weight_and_sum(g1, g1_node_feats)
tensor([[1.2194, 0.9490, 1.3235, 0.9609, 0.7710]],
       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])
>>>
>>> weight_and_sum(batch_g, batch_f)
tensor([[1.2194, 0.9490, 1.3235, 0.9609, 0.7710],
        [0.5322, 0.5840, 1.0729, 1.3665, 1.2360]],
       grad_fn=<SegmentReduceBackward>)

Notes

WeightAndSum module was commonly used in molecular property prediction networks, see the GCN predictor in dgl-lifesci to understand how to use WeightAndSum layer to get the graph readout output.

forward(g, feats)

Compute molecule representations out of atom representations

Parameters:

• g (DGLGraph) – DGLGraph with batch size B for processing multiple molecules in parallel

• feats (FloatTensor of shape (N, self.in_feats)) – Representations for all atoms in the molecules * N is the total number of atoms in all molecules

Returns:

Representations for B molecules

Return type:

FloatTensor of shape (B, self.in_feats)