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>
75 行
2.5 KiB
Python
75 行
2.5 KiB
Python
"""Torch Module for Attention-based Graph Neural Network layer"""
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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 functional as F
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from .... import function as fn
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from ..softmax import edge_softmax
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from ....utils import expand_as_pair
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class AGNNConv(nn.Module):
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r"""Attention-based Graph Neural Network layer from paper `Attention-based
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Graph Neural Network for Semi-Supervised Learning
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<https://arxiv.org/abs/1803.03735>`__.
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.. math::
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H^{l+1} = P H^{l}
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where :math:`P` is computed as:
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.. math::
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P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l))
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Parameters
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----------
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init_beta : float, optional
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The :math:`\beta` in the formula.
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learn_beta : bool, optional
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If True, :math:`\beta` will be learnable parameter.
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"""
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def __init__(self,
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init_beta=1.,
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learn_beta=True):
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super(AGNNConv, self).__init__()
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if learn_beta:
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self.beta = nn.Parameter(th.Tensor([init_beta]))
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else:
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self.register_buffer('beta', th.Tensor([init_beta]))
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def forward(self, graph, feat):
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r"""Compute AGNN layer.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : torch.Tensor
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The input feature of shape :math:`(N, *)` :math:`N` is the
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number of nodes, and :math:`*` could be of any shape.
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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}, *)` and :math:`(N_{out}, *})`, the the :math:`*` in the later
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tensor must equal the previous one.
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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, *)` where :math:`*`
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should be the same as input shape.
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"""
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graph = graph.local_var()
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feat_src, feat_dst = expand_as_pair(feat)
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graph.srcdata['h'] = feat_src
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graph.srcdata['norm_h'] = F.normalize(feat_src, p=2, dim=-1)
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if isinstance(feat, tuple):
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graph.dstdata['norm_h'] = F.normalize(feat_dst, p=2, dim=-1)
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# compute cosine distance
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graph.apply_edges(fn.u_dot_v('norm_h', 'norm_h', 'cos'))
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cos = graph.edata.pop('cos')
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e = self.beta * cos
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graph.edata['p'] = edge_softmax(graph, e)
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graph.update_all(fn.u_mul_e('h', 'p', 'm'), fn.sum('m', 'h'))
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return graph.dstdata.pop('h')
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