wehub-resource-sync
34 lines
971 B
Python
Executable File
34 lines
971 B
Python
Executable File
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Bundler(nn.Module):
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"""
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Bundler, which will be the node_apply function in DGL paradigm
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"""
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def __init__(self, in_feats, out_feats, activation, dropout, bias=True):
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super(Bundler, self).__init__()
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self.dropout = nn.Dropout(p=dropout)
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self.linear = nn.Linear(in_feats * 2, out_feats, bias)
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self.activation = activation
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nn.init.xavier_uniform_(
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self.linear.weight, gain=nn.init.calculate_gain("relu")
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)
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def concat(self, h, aggre_result):
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bundle = torch.cat((h, aggre_result), 1)
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bundle = self.linear(bundle)
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return bundle
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def forward(self, node):
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h = node.data["h"]
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c = node.data["c"]
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bundle = self.concat(h, c)
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bundle = F.normalize(bundle, p=2, dim=1)
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if self.activation:
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bundle = self.activation(bundle)
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return {"h": bundle}
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