wehub-resource-sync
139 lines
4.6 KiB
Python
139 lines
4.6 KiB
Python
"""Torch Module for DenseSAGEConv"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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from torch import nn
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from ....utils import check_eq_shape
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class DenseSAGEConv(nn.Module):
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"""GraphSAGE layer from `Inductive Representation Learning on Large Graphs
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<https://arxiv.org/abs/1706.02216>`__
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We recommend to use this module when appying GraphSAGE on dense graphs.
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Note that we only support gcn aggregator in DenseSAGEConv.
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Parameters
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----------
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in_feats : int
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Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`.
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out_feats : int
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Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`.
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feat_drop : float, optional
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Dropout rate on features. Default: 0.
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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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norm : callable activation function/layer or None, optional
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If not None, applies normalization to the updated node features.
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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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Example
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-------
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>>> import dgl
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>>> import numpy as np
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>>> import torch as th
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>>> from dgl.nn import DenseSAGEConv
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>>>
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>>> feat = th.ones(6, 10)
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>>> adj = th.tensor([[0., 0., 1., 0., 0., 0.],
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... [1., 0., 0., 0., 0., 0.],
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... [0., 1., 0., 0., 0., 0.],
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... [0., 0., 1., 0., 0., 1.],
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... [0., 0., 0., 1., 0., 0.],
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... [0., 0., 0., 0., 0., 0.]])
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>>> conv = DenseSAGEConv(10, 2)
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>>> res = conv(adj, feat)
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>>> res
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tensor([[1.0401, 2.1008],
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[1.0401, 2.1008],
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[1.0401, 2.1008],
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[1.0401, 2.1008],
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[1.0401, 2.1008],
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[1.0401, 2.1008]], grad_fn=<AddmmBackward>)
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See also
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--------
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`SAGEConv <https://docs.dgl.ai/api/python/nn.pytorch.html#sageconv>`__
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"""
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def __init__(
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self,
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in_feats,
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out_feats,
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feat_drop=0.0,
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bias=True,
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norm=None,
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activation=None,
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):
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super(DenseSAGEConv, 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.feat_drop = nn.Dropout(feat_drop)
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self.activation = activation
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self.fc = nn.Linear(in_feats, out_feats, bias=bias)
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self.reset_parameters()
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def reset_parameters(self):
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r"""
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Description
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-----------
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Reinitialize learnable parameters.
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Notes
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-----
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The linear weights :math:`W^{(l)}` are initialized using Glorot uniform initialization.
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"""
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gain = nn.init.calculate_gain("relu")
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nn.init.xavier_uniform_(self.fc.weight, gain=gain)
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def forward(self, adj, feat):
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r"""
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Description
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-----------
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Compute (Dense) Graph SAGE 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 SAGE 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 or a pair of torch.Tensor
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If a torch.Tensor is given, the input feature of shape :math:`(N, D_{in})` where
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:math:`D_{in}` is size of input feature, :math:`N` is the number of nodes.
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If a pair of torch.Tensor is given, the pair must contain two tensors of shape
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:math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`.
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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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check_eq_shape(feat)
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if isinstance(feat, tuple):
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feat_src = self.feat_drop(feat[0])
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feat_dst = self.feat_drop(feat[1])
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else:
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feat_src = feat_dst = self.feat_drop(feat)
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adj = adj.to(feat_src)
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in_degrees = adj.sum(dim=1, keepdim=True)
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h_neigh = (adj @ feat_src + feat_dst) / (in_degrees + 1)
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rst = self.fc(h_neigh)
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# activation
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if self.activation is not None:
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rst = self.activation(rst)
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# normalization
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if self._norm is not None:
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rst = self._norm(rst)
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return rst
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