wehub-resource-sync
68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
|
|
from easygraph.classes import Hypergraph
|
|
from easygraph.nn import HyperGCNConv
|
|
|
|
|
|
class HyperGCN(nn.Module):
|
|
r"""The HyperGCN model 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).
|
|
|
|
Parameters:
|
|
``in_channels`` (``int``): :math:`C_{in}` is the number of input channels.
|
|
``hid_channels`` (``int``): :math:`C_{hid}` is the number of hidden channels.
|
|
``num_classes`` (``int``): The Number of class of the classification task.
|
|
``use_mediator`` (``str``): Whether to use mediator to transform the hyperedges to edges in the graph. Defaults to ``False``.
|
|
``fast`` (``bool``): If set to ``True``, the transformed graph structure will be computed once from the input hypergraph and vertex features, and cached for future use. Defaults to ``True``.
|
|
``drop_rate`` (``float``, optional): Dropout ratio. Defaults to 0.5.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
hid_channels: int,
|
|
num_classes: int,
|
|
use_mediator: bool = False,
|
|
use_bn: bool = False,
|
|
fast: bool = True,
|
|
drop_rate: float = 0.5,
|
|
) -> None:
|
|
super().__init__()
|
|
self.fast = fast
|
|
self.cached_g = None
|
|
self.with_mediator = use_mediator
|
|
self.layers = nn.ModuleList()
|
|
self.layers.append(
|
|
HyperGCNConv(
|
|
in_channels,
|
|
hid_channels,
|
|
use_mediator,
|
|
use_bn=use_bn,
|
|
drop_rate=drop_rate,
|
|
)
|
|
)
|
|
self.layers.append(
|
|
HyperGCNConv(
|
|
hid_channels, num_classes, use_mediator, use_bn=use_bn, is_last=True
|
|
)
|
|
)
|
|
|
|
def forward(self, X: torch.Tensor, hg: "eg.Hypergraph") -> torch.Tensor:
|
|
r"""The forward function.
|
|
|
|
Parameters:
|
|
``X`` (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.
|
|
``hg`` (``eg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices.
|
|
"""
|
|
if self.fast:
|
|
if self.cached_g is None:
|
|
self.cached_g = Hypergraph.from_hypergraph_hypergcn(
|
|
hg, X, self.with_mediator
|
|
)
|
|
for layer in self.layers:
|
|
X = layer(X, hg, self.cached_g)
|
|
else:
|
|
for layer in self.layers:
|
|
X = layer(X, hg)
|
|
return X
|