import torch
import torch.nn as nn
from dhg.structure.graphs import Graph
[docs]class GCNConv(nn.Module):
r"""The GCN convolution layer proposed in `Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/pdf/1609.02907>`_ paper (ICLR 2017).
Matrix Format:
.. math::
\mathbf{X}^{\prime} = \sigma \left( \mathbf{\hat{D}}^{-\frac{1}{2}} \mathbf{\hat{A}}
\mathbf{\hat{D}}^{-\frac{1}{2}} \mathbf{X} \mathbf{\Theta} \right),
where :math:`\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}` and :math:`\mathbf{\hat{D}}_{ii} = \sum_j \mathbf{\hat{A}}_{ij}`.
Args:
``in_channels`` (``int``): :math:`C_{in}` is the number of input channels.
``out_channels`` (``int``): :math:`C_{out}` is the number of output channels.
``bias`` (``bool``): If set to ``False``, the layer will not learn the bias parameter. Defaults to ``True``.
``use_bn`` (``bool``): If set to ``True``, the layer will use batch normalization. Defaults to ``False``.
``drop_rate`` (``float``): The dropout probability. If ``dropout <= 0``, the layer will not drop values. Defaults to ``0.5``.
``is_last`` (``bool``): If set to ``True``, the layer will not apply the final activation and dropout functions. Defaults to ``False``.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
bias: bool = True,
use_bn: bool = False,
drop_rate: float = 0.5,
is_last: bool = False,
):
super().__init__()
self.is_last = is_last
self.bn = nn.BatchNorm1d(out_channels) if use_bn else None
self.act = nn.ReLU(inplace=True)
self.drop = nn.Dropout(drop_rate)
self.theta = nn.Linear(in_channels, out_channels, bias=bias)
[docs] def forward(self, X: torch.Tensor, g: Graph) -> torch.Tensor:
r"""The forward function.
Args:
X (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.
g (``dhg.Graph``): The graph structure that contains :math:`N` vertices.
"""
X = self.theta(X)
X = g.smoothing_with_GCN(X)
if not self.is_last:
X = self.act(X)
if self.bn is not None:
X = self.bn(X)
X = self.drop(X)
return X