wehub-resource-sync
62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
from typing import Optional
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import torch
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import torch.nn as nn
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from easygraph.classes import Graph
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from easygraph.classes import Hypergraph
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class HyperGCNConv(nn.Module):
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r"""The HyperGCN convolution layer proposed in `HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs <https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf>`_ paper (NeurIPS 2019).
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Parameters:
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``in_channels`` (``int``): :math:`C_{in}` is the number of input channels.
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``out_channels`` (int): :math:`C_{out}` is the number of output channels.
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``use_mediator`` (``str``): Whether to use mediator to transform the hyperedges to edges in the graph. Defaults to ``False``.
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``bias`` (``bool``): If set to ``False``, the layer will not learn the bias parameter. Defaults to ``True``.
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``use_bn`` (``bool``): If set to ``True``, the layer will use batch normalization. Defaults to ``False``.
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``drop_rate`` (``float``): If set to a positive number, the layer will use dropout. Defaults to ``0.5``.
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``is_last`` (``bool``): If set to ``True``, the layer will not apply the final activation and dropout functions. Defaults to ``False``.
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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use_mediator: bool = False,
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bias: bool = True,
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use_bn: bool = False,
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drop_rate: float = 0.5,
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is_last: bool = False,
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):
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super().__init__()
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self.is_last = is_last
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self.bn = nn.BatchNorm1d(out_channels) if use_bn else None
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self.use_mediator = use_mediator
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self.act = nn.ReLU(inplace=True)
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self.drop = nn.Dropout(drop_rate)
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self.theta = nn.Linear(in_channels, out_channels, bias=bias)
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def forward(
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self, X: torch.Tensor, hg: Hypergraph, cached_g: Optional[Graph] = None
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) -> torch.Tensor:
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r"""The forward function.
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Parameters:
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``X`` (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.
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``hg`` (``eg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices.
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``cached_g`` (``eg.Graph``): The pre-transformed graph structure from the hypergraph structure that contains :math:`N` vertices. If not provided, the graph structure will be transformed for each forward time. Defaults to ``None``.
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"""
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X = self.theta(X)
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if self.bn is not None:
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X = self.bn(X)
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if cached_g is None:
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g = Graph.from_hypergraph_hypergcn(hg, X, self.use_mediator)
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X = g.smoothing_with_GCN(X)
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else:
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X = cached_g.smoothing_with_GCN(X)
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if not self.is_last:
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X = self.drop(self.act(X))
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return X
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