dmlc--dgl
xnouhz
756fdd8e90
* [example] deepergcn * update * update * update * update * update Co-authored-by: Mufei Li <mufeili1996@gmail.com>
87 行
2.6 KiB
Python
87 行
2.6 KiB
Python
import torch.nn as nn
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import torch.nn.functional as F
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import dgl.function as fn
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from ogb.graphproppred.mol_encoder import AtomEncoder
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from dgl.nn.pytorch.glob import AvgPooling
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from layers import GENConv
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class DeeperGCN(nn.Module):
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r"""
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Description
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-----------
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Introduced in "DeeperGCN: All You Need to Train Deeper GCNs <https://arxiv.org/abs/2006.07739>"
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Parameters
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----------
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node_feat_dim: int
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Size of node feature.
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edge_feat_dim: int
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Size of edge feature.
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hid_dim: int
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Size of hidden representations.
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out_dim: int
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Size of output.
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num_layers: int
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Number of graph convolutional layers.
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dropout: float
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Dropout rate. Default is 0.
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beta: float
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A continuous variable called an inverse temperature. Default is 1.0.
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learn_beta: bool
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Whether beta is a learnable weight. Default is False.
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aggr: str
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Type of aggregation. Default is 'softmax'.
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mlp_layers: int
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Number of MLP layers in message normalization. Default is 1.
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"""
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def __init__(self,
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node_feat_dim,
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edge_feat_dim,
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hid_dim,
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out_dim,
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num_layers,
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dropout=0.,
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beta=1.0,
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learn_beta=False,
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aggr='softmax',
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mlp_layers=1):
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super(DeeperGCN, self).__init__()
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self.num_layers = num_layers
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self.dropout = dropout
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self.gcns = nn.ModuleList()
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self.norms = nn.ModuleList()
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for _ in range(self.num_layers):
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conv = GENConv(in_dim=hid_dim,
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out_dim=hid_dim,
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aggregator=aggr,
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beta=beta,
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learn_beta=learn_beta,
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mlp_layers=mlp_layers)
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self.gcns.append(conv)
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self.norms.append(nn.BatchNorm1d(hid_dim, affine=True))
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self.node_encoder = AtomEncoder(hid_dim)
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self.pooling = AvgPooling()
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self.output = nn.Linear(hid_dim, out_dim)
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def forward(self, g, edge_feats, node_feats=None):
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with g.local_scope():
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hv = self.node_encoder(node_feats)
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he = edge_feats
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for layer in range(self.num_layers):
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hv1 = self.norms[layer](hv)
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hv1 = F.relu(hv1)
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hv1 = F.dropout(hv1, p=self.dropout, training=self.training)
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hv = self.gcns[layer](g, hv1, he) + hv
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h_g = self.pooling(g, hv)
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return self.output(h_g)
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