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:
torch.nn.modules.module.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>)
See also
Notes
SetTransformerEncoder is not a readout layer, the tensor it returned is nodewise representation instead out graphwise representation, and the SetTransformerDecoder would return a graph readout tensor.
-
forward
(graph, feat)[source]¶ Compute the Encoder part of Set Transformer.
- 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.
- Returns
The output feature with shape \((N, D)\).
- Return type
torch.Tensor