dmlc--dgl
xnouhz
756fdd8e90
* [example] deepergcn * update * update * update * update * update Co-authored-by: Mufei Li <mufeili1996@gmail.com>
50 行
1.5 KiB
Python
50 行
1.5 KiB
Python
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 MLP(nn.Sequential):
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r"""
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Description
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-----------
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From equation (5) in "DeeperGCN: All You Need to Train Deeper GCNs <https://arxiv.org/abs/2006.07739>"
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"""
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def __init__(self,
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channels,
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act='relu',
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dropout=0.,
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bias=True):
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layers = []
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for i in range(1, len(channels)):
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layers.append(nn.Linear(channels[i - 1], channels[i], bias))
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if i < len(channels) - 1:
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layers.append(nn.BatchNorm1d(channels[i], affine=True))
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layers.append(nn.ReLU())
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layers.append(nn.Dropout(dropout))
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super(MLP, self).__init__(*layers)
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class MessageNorm(nn.Module):
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r"""
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Description
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-----------
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Message normalization was 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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learn_scale: bool
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Whether s is a learnable scaling factor or not. Default is False.
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"""
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def __init__(self, learn_scale=False):
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super(MessageNorm, self).__init__()
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self.scale = nn.Parameter(torch.FloatTensor([1.0]), requires_grad=learn_scale)
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def forward(self, feats, msg, p=2):
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msg = F.normalize(msg, p=2, dim=-1)
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feats_norm = feats.norm(p=p, dim=-1, keepdim=True)
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return msg * feats_norm * self.scale
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