import torch
import torch.nn as nn
from graphwar.nn.layers import RobustConv, activations, Sequential
from graphwar.utils import wrapper
[docs]class RobustGCN(nn.Module):
r"""Robust graph convolutional network (RobustGCN)
from the `"Robust Graph Convolutional Networks
Against Adversarial Attacks"
<http://pengcui.thumedialab.com/papers/RGCN.pdf>`_ paper (KDD'19)
Parameters
----------
in_channels : int,
the input dimensions of model
out_channels : int,
the output dimensions of model
hids : list, optional
the number of hidden units for each hidden layer, by default [32]
acts : list, optional
the activation function for each hidden layer, by default ['relu']
dropout : float, optional
the dropout ratio of model, by default 0.5
bias : bool, optional
whether to use bias in the layers, by default True
gamma : float, optional
the scale of attention on the variances, by default 1.0
bn: bool, optional
whether to use :class:`BatchNorm1d` after the convolution layer, by default False
Note
----
It is convenient to extend the number of layers with different or the same
hidden units (activation functions) using :meth:`graphwar.utils.wrapper`.
See Examples below:
Examples
--------
>>> # RobustGCN with one hidden layer
>>> model = RobustGCN(100, 10)
>>> # RobustGCN with two hidden layers
>>> model = RobustGCN(100, 10, hids=[32, 16], acts=['relu', 'elu'])
>>> # RobustGCN with two hidden layers, without activation at the first layer
>>> model = RobustGCN(100, 10, hids=[32, 16], acts=[None, 'relu'])
>>> # RobustGCN with very deep architectures, each layer has elu as activation function
>>> model = RobustGCN(100, 10, hids=[16]*8, acts=['elu'])
See also
--------
:class:`graphwar.nn.layers.RobustConv`
"""
@wrapper
def __init__(self,
in_channels: int,
out_channels: int,
hids: list = [32],
acts: list = ['relu'],
dropout: float = 0.5,
bias: bool = True,
gamma: float = 1.0,
bn: bool = False,):
super().__init__()
assert len(hids) > 0
self.conv1 = RobustConv(in_channels,
hids[0],
bias=bias)
self.act1 = activations.get(acts[0])
conv2 = nn.ModuleList()
in_channels = hids[0]
for hid, act in zip(hids[1:], acts[1:]):
conv2.append(RobustConv(in_channels,
hid,
bias=bias,
gamma=gamma))
if bn:
conv.append(nn.BatchNorm1d(hid))
conv2.append(activations.get(act))
in_channels = hid
conv2.append(RobustConv(
in_channels, out_channels, gamma=gamma, bias=bias))
self.conv2 = conv2
self.dropout = nn.Dropout(dropout)
[docs] def reset_parameters(self):
self.conv1.reset_parameters()
for conv in self.conv2:
conv.reset_parameters()
self.cache_clear()
[docs] def cache_clear(self):
"""Clear cached inputs or intermediate results."""
self.mean = self.var = None
return self
[docs] def forward(self, x, edge_index, edge_weight=None):
x = self.dropout(x)
mean, var = self.conv1(x, edge_index, edge_weight)
mean, var = self.act1(mean), self.act1(var)
self.mean, self.var = mean, var
for conv in self.conv2:
if isinstance(conv, RobustConv):
mean, var = self.dropout(mean), self.dropout(var)
mean, var = conv((mean, var), edge_index, edge_weight)
else:
mean, var = conv(mean), conv(var)
std = torch.sqrt(var + 1e-8)
eps = torch.randn_like(std)
z = eps.mul(std).add_(mean)
return z