dmlc--dgl
Zihao Ye
af61e2fbb4
* init gat * fix * gin * 7 nn modules * rename & lint * upd * upd * fix lint * upd test * upd * lint * shape check * upd * lint * address comments * update tensorflow Co-authored-by: Quan Gan <coin2028@hotmail.com> Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com> Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
117 行
4.0 KiB
Python
117 行
4.0 KiB
Python
"""Torch Module for DenseGraphConv"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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import torch as th
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from torch import nn
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from torch.nn import init
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class DenseGraphConv(nn.Module):
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"""Graph Convolutional Network layer where the graph structure
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is given by an adjacency matrix.
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We recommend user to use this module when applying graph convolution on
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dense graphs.
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Parameters
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----------
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in_feats : int
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Input feature size.
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out_feats : int
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Output feature size.
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norm : str, optional
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How to apply the normalizer. If is `'right'`, divide the aggregated messages
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by each node's in-degrees, which is equivalent to averaging the received messages.
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If is `'none'`, no normalization is applied. Default is `'both'`,
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where the :math:`c_{ij}` in the paper is applied.
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bias : bool
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If True, adds a learnable bias to the output. Default: ``True``.
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activation : callable activation function/layer or None, optional
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If not None, applies an activation function to the updated node features.
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Default: ``None``.
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See also
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--------
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GraphConv
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"""
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def __init__(self,
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in_feats,
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out_feats,
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norm='both',
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bias=True,
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activation=None):
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super(DenseGraphConv, self).__init__()
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self._in_feats = in_feats
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self._out_feats = out_feats
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self._norm = norm
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self.weight = nn.Parameter(th.Tensor(in_feats, out_feats))
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if bias:
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self.bias = nn.Parameter(th.Tensor(out_feats))
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else:
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self.register_buffer('bias', None)
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self.reset_parameters()
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self._activation = activation
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def reset_parameters(self):
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"""Reinitialize learnable parameters."""
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init.xavier_uniform_(self.weight)
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if self.bias is not None:
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init.zeros_(self.bias)
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def forward(self, adj, feat):
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r"""Compute (Dense) Graph Convolution layer.
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Parameters
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----------
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adj : torch.Tensor
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The adjacency matrix of the graph to apply Graph Convolution on, when
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applied to a unidirectional bipartite graph, ``adj`` should be of shape
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should be of shape :math:`(N_{out}, N_{in})`; when applied to a homo
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graph, ``adj`` should be of shape :math:`(N, N)`. In both cases,
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a row represents a destination node while a column represents a source
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node.
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feat : torch.Tensor
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The input feature.
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Returns
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-------
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torch.Tensor
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The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
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is size of output feature.
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"""
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adj = adj.float().to(feat.device)
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src_degrees = adj.sum(dim=0).clamp(min=1)
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dst_degrees = adj.sum(dim=1).clamp(min=1)
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feat_src = feat
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if self._norm == 'both':
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norm_src = th.pow(src_degrees, -0.5)
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shp = norm_src.shape + (1,) * (feat.dim() - 1)
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norm_src = th.reshape(norm_src, shp).to(feat.device)
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feat_src = feat_src * norm_src
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if self._in_feats > self._out_feats:
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# mult W first to reduce the feature size for aggregation.
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feat_src = th.matmul(feat_src, self.weight)
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rst = adj @ feat_src
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else:
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# aggregate first then mult W
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rst = adj @ feat_src
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rst = th.matmul(rst, self.weight)
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if self._norm != 'none':
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if self._norm == 'both':
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norm_dst = th.pow(dst_degrees, -0.5)
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else: # right
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norm_dst = 1.0 / dst_degrees
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shp = norm_dst.shape + (1,) * (feat.dim() - 1)
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norm_dst = th.reshape(norm_dst, shp).to(feat.device)
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rst = rst * norm_dst
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if self.bias is not None:
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rst = rst + self.bias
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if self._activation is not None:
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rst = self._activation(rst)
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return rst
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