from typing import List
import torch.nn as nn
from greatx.defense import GNNGUARD as GNNGUARDLayer
from greatx.nn.layers import GCNConv, Sequential, activations
from greatx.utils import wrapper
[docs]class GNNGUARD(nn.Module):
r"""Graph Convolution Network (GCN) with
:class:`greatx.defense.GNNGUARD` from the `"GNNGUARD:
Defending Graph Neural Networks against Adversarial Attacks"
<https://arxiv.org/abs/2006.08149>`_ paper (NeurIPS'20)
Parameters
----------
in_channels : int,
the input dimensions of model
out_channels : int,
the output dimensions of model
hids : List[int], optional
the number of hidden units for each hidden layer,
by default [16]
acts : List[str], 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
bn: bool, optional
whether to use :class:`BatchNorm1d` after the convolution layer,
by default False
Examples
--------
>>> # GNNGUARD with one hidden layer
>>> model = GNNGUARD(100, 10)
>>> # GNNGUARD with two hidden layers
>>> model = GNNGUARD(100, 10, hids=[32, 16], acts=['relu', 'elu'])
>>> # GNNGUARD with two hidden layers, without first activation
>>> model = GNNGUARD(100, 10, hids=[32, 16], acts=[None, 'relu'])
>>> # GNNGUARD with deep architectures, each layer has elu activation
>>> model = GNNGUARD(100, 10, hids=[16]*8, acts=['elu'])
See also
--------
:class:`greatx.defense.GNNGUARD`
:class:`greatx.nn.models.supervised.GCN`
"""
@wrapper
def __init__(self, in_channels: int, out_channels: int,
hids: List[int] = [16], acts: List[str] = ['relu'],
dropout: float = 0.5, bn: bool = False,
normalize: bool = True, bias: bool = True):
super().__init__()
conv = []
# Add self-loops in the first input layer
conv.append(GNNGUARDLayer(add_self_loops=True))
for hid, act in zip(hids, acts):
conv.append(
GCNConv(in_channels, hid, bias=bias, add_self_loops=False,
normalize=normalize))
if bn:
conv.append(nn.BatchNorm1d(hid))
conv.append(activations.get(act))
conv.append(GNNGUARDLayer())
conv.append(nn.Dropout(dropout))
in_channels = hid
conv.append(
GCNConv(in_channels, out_channels, add_self_loops=False, bias=bias,
normalize=normalize))
self.conv = Sequential(*conv)
[docs] def reset_parameters(self):
self.conv.reset_parameters()
[docs] def forward(self, x, edge_index, edge_weight=None):
""""""
for layer in self.conv:
if isinstance(layer, GNNGUARDLayer):
edge_index, edge_weight = layer(x, edge_index)
elif isinstance(layer, GCNConv):
x = layer(x, edge_index, edge_weight)
else:
x = layer(x)
return x