SetTransformerEncoder

class dgl.nn.pytorch.glob.SetTransformerEncoder(d_model, n_heads, d_head, d_ff, n_layers=1, block_type='sab', m=None, dropouth=0.0, dropouta=0.0)[source]

Bases: Module

The Encoder module from Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

Parameters:

d_model (int) – The hidden size of the model.
n_heads (int) – The number of heads.
d_head (int) – The hidden size of each head.
d_ff (int) – The kernel size in FFN (Positionwise Feed-Forward Network) layer.
n_layers (int) – The number of layers.
block_type (str) – Building block type: ‘sab’ (Set Attention Block) or ‘isab’ (Induced Set Attention Block).
m (int or None) – The number of induced vectors in ISAB Block. Set to None if block type is ‘sab’.
dropouth (float) – The dropout rate of each sublayer.
dropouta (float) – The dropout rate of attention heads.

Examples

>>> import dgl
>>> import torch as th
>>> from dgl.nn import SetTransformerEncoder
>>>
>>> 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]])
>>>
>>> set_trans_enc = SetTransformerEncoder(5, 4, 4, 20)  # create a settrans encoder.

Case 1: Input a single graph

>>> set_trans_enc(g1, g1_node_feats)
tensor([[ 0.1262, -1.9081,  0.7287,  0.1678,  0.8854],
        [-0.0634, -1.1996,  0.6955, -0.9230,  1.4904],
        [-0.9972, -0.7924,  0.6907, -0.5221,  1.6211]],
       grad_fn=<NativeLayerNormBackward>)

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])
>>>
>>> set_trans_enc(batch_g, batch_f)
tensor([[ 0.1262, -1.9081,  0.7287,  0.1678,  0.8854],
        [-0.0634, -1.1996,  0.6955, -0.9230,  1.4904],
        [-0.9972, -0.7924,  0.6907, -0.5221,  1.6211],
        [-0.7973, -1.3203,  0.0634,  0.5237,  1.5306],
        [-0.4497, -1.0920,  0.8470, -0.8030,  1.4977],
        [-0.4940, -1.6045,  0.2363,  0.4885,  1.3737],
        [-0.9840, -1.0913, -0.0099,  0.4653,  1.6199]],
       grad_fn=<NativeLayerNormBackward>)