wehub-resource-sync
146 lines
5.0 KiB
Python
146 lines
5.0 KiB
Python
"""Torch Module for DenseGraphConv"""
|
|
# pylint: disable= no-member, arguments-differ, invalid-name
|
|
import torch as th
|
|
from torch import nn
|
|
from torch.nn import init
|
|
|
|
|
|
class DenseGraphConv(nn.Module):
|
|
"""Graph Convolutional layer from `Semi-Supervised Classification with Graph
|
|
Convolutional Networks <https://arxiv.org/abs/1609.02907>`__
|
|
|
|
We recommend user to use this module when applying graph convolution on
|
|
dense graphs.
|
|
|
|
Parameters
|
|
----------
|
|
in_feats : int
|
|
Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`.
|
|
out_feats : int
|
|
Output feature size; i.e., the number of dimensions of :math:`h_i^{(l+1)}`.
|
|
norm : str, optional
|
|
How to apply the normalizer. If is `'right'`, divide the aggregated messages
|
|
by each node's in-degrees, which is equivalent to averaging the received messages.
|
|
If is `'none'`, no normalization is applied. Default is `'both'`,
|
|
where the :math:`c_{ij}` in the paper is applied.
|
|
bias : bool, optional
|
|
If True, adds a learnable bias to the output. Default: ``True``.
|
|
activation : callable activation function/layer or None, optional
|
|
If not None, applies an activation function to the updated node features.
|
|
Default: ``None``.
|
|
|
|
Notes
|
|
-----
|
|
Zero in-degree nodes will lead to all-zero output. A common practice
|
|
to avoid this is to add a self-loop for each node in the graph,
|
|
which can be achieved by setting the diagonal of the adjacency matrix to be 1.
|
|
|
|
Example
|
|
-------
|
|
>>> import dgl
|
|
>>> import numpy as np
|
|
>>> import torch as th
|
|
>>> from dgl.nn import DenseGraphConv
|
|
>>>
|
|
>>> feat = th.ones(6, 10)
|
|
>>> adj = th.tensor([[0., 0., 1., 0., 0., 0.],
|
|
... [1., 0., 0., 0., 0., 0.],
|
|
... [0., 1., 0., 0., 0., 0.],
|
|
... [0., 0., 1., 0., 0., 1.],
|
|
... [0., 0., 0., 1., 0., 0.],
|
|
... [0., 0., 0., 0., 0., 0.]])
|
|
>>> conv = DenseGraphConv(10, 2)
|
|
>>> res = conv(adj, feat)
|
|
>>> res
|
|
tensor([[0.2159, 1.9027],
|
|
[0.3053, 2.6908],
|
|
[0.3053, 2.6908],
|
|
[0.3685, 3.2481],
|
|
[0.3053, 2.6908],
|
|
[0.0000, 0.0000]], grad_fn=<AddBackward0>)
|
|
|
|
See also
|
|
--------
|
|
`GraphConv <https://docs.dgl.ai/api/python/nn.pytorch.html#graphconv>`__
|
|
"""
|
|
|
|
def __init__(
|
|
self, in_feats, out_feats, norm="both", bias=True, activation=None
|
|
):
|
|
super(DenseGraphConv, self).__init__()
|
|
self._in_feats = in_feats
|
|
self._out_feats = out_feats
|
|
self._norm = norm
|
|
self.weight = nn.Parameter(th.Tensor(in_feats, out_feats))
|
|
if bias:
|
|
self.bias = nn.Parameter(th.Tensor(out_feats))
|
|
else:
|
|
self.register_buffer("bias", None)
|
|
|
|
self.reset_parameters()
|
|
self._activation = activation
|
|
|
|
def reset_parameters(self):
|
|
"""Reinitialize learnable parameters."""
|
|
init.xavier_uniform_(self.weight)
|
|
if self.bias is not None:
|
|
init.zeros_(self.bias)
|
|
|
|
def forward(self, adj, feat):
|
|
r"""Compute (Dense) Graph Convolution layer.
|
|
|
|
Parameters
|
|
----------
|
|
adj : torch.Tensor
|
|
The adjacency matrix of the graph to apply Graph Convolution on, when
|
|
applied to a unidirectional bipartite graph, ``adj`` should be of shape
|
|
should be of shape :math:`(N_{out}, N_{in})`; when applied to a homo
|
|
graph, ``adj`` should be of shape :math:`(N, N)`. In both cases,
|
|
a row represents a destination node while a column represents a source
|
|
node.
|
|
feat : torch.Tensor
|
|
The input feature.
|
|
|
|
Returns
|
|
-------
|
|
torch.Tensor
|
|
The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
|
|
is size of output feature.
|
|
"""
|
|
adj = adj.to(feat)
|
|
src_degrees = adj.sum(dim=0).clamp(min=1)
|
|
dst_degrees = adj.sum(dim=1).clamp(min=1)
|
|
feat_src = feat
|
|
|
|
if self._norm == "both":
|
|
norm_src = th.pow(src_degrees, -0.5)
|
|
shp = norm_src.shape + (1,) * (feat.dim() - 1)
|
|
norm_src = th.reshape(norm_src, shp).to(feat.device)
|
|
feat_src = feat_src * norm_src
|
|
|
|
if self._in_feats > self._out_feats:
|
|
# mult W first to reduce the feature size for aggregation.
|
|
feat_src = th.matmul(feat_src, self.weight)
|
|
rst = adj @ feat_src
|
|
else:
|
|
# aggregate first then mult W
|
|
rst = adj @ feat_src
|
|
rst = th.matmul(rst, self.weight)
|
|
|
|
if self._norm != "none":
|
|
if self._norm == "both":
|
|
norm_dst = th.pow(dst_degrees, -0.5)
|
|
else: # right
|
|
norm_dst = 1.0 / dst_degrees
|
|
shp = norm_dst.shape + (1,) * (feat.dim() - 1)
|
|
norm_dst = th.reshape(norm_dst, shp).to(feat.device)
|
|
rst = rst * norm_dst
|
|
|
|
if self.bias is not None:
|
|
rst = rst + self.bias
|
|
|
|
if self._activation is not None:
|
|
rst = self._activation(rst)
|
|
|
|
return rst
|