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'''
Adversarial Attacks on Neural Networks for Graph Data. ICML 2018.
https://arxiv.org/abs/1806.02371
Author's Implementation
https://github.com/Hanjun-Dai/graph_adversarial_attack
This part of code is adopted from the author's implementation (Copyright (c) 2018 Dai, Hanjun and Li, Hui and Tian, Tian and Huang, Xin and Wang, Lin and Zhu, Jun and Song, Le) but modified
to be integrated into the repository.
'''
import os
import sys
import numpy as np
import torch
import networkx as nx
import random
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
from deeprobust.graph.rl.env import GraphNormTool
class QNetNode(nn.Module):
def __init__(self, node_features, node_labels, list_action_space, n_injected, bilin_q=1, embed_dim=64, mlp_hidden=64, max_lv=1, gm='mean_field', device='cpu'):
'''
bilin_q: bilinear q or not
mlp_hidden: mlp hidden layer size
mav_lv: max rounds of message passing
'''
super(QNetNode, self).__init__()
self.node_features = node_features
self.identity = torch.eye(node_labels.max() + 1).to(node_labels.device)
# self.node_labels = self.to_onehot(node_labels)
self.n_injected = n_injected
self.list_action_space = list_action_space
self.total_nodes = len(list_action_space)
self.bilin_q = bilin_q
self.embed_dim = embed_dim
self.mlp_hidden = mlp_hidden
self.max_lv = max_lv
self.gm = gm
if mlp_hidden:
self.linear_1 = nn.Linear(embed_dim * 3, mlp_hidden)
self.linear_out = nn.Linear(mlp_hidden, 1)
else:
self.linear_out = nn.Linear(embed_dim * 3, 1)
self.w_n2l = Parameter(torch.Tensor(node_features.size()[1], embed_dim))
self.bias_n2l = Parameter(torch.Tensor(embed_dim))
# self.bias_picked = Parameter(torch.Tensor(1, embed_dim))
self.conv_params = nn.Linear(embed_dim, embed_dim)
self.norm_tool = GraphNormTool(normalize=True, gm=self.gm, device=device)
weights_init(self)
input_dim = (node_labels.max() + 1) * self.n_injected
self.label_encoder_1 = nn.Linear(input_dim, mlp_hidden)
self.label_encoder_2 = nn.Linear(mlp_hidden, embed_dim)
self.device = self.node_features.device
def to_onehot(self, labels):
return self.identity[labels].view(-1, self.identity.shape[1])
def get_label_embedding(self, labels):
# int to one hot
onehot = self.to_onehot(labels).view(1, -1)
x = F.relu(self.label_encoder_1(onehot))
x = F.relu(self.label_encoder_2(x))
return x
def get_action_label_encoding(self, label):
onehot = self.to_onehot(label)
zeros = torch.zeros((onehot.shape[0], self.embed_dim - onehot.shape[1])).to(onehot.device)
return torch.cat((onehot, zeros), dim=1)
def get_graph_embedding(self, adj):
if self.node_features.data.is_sparse:
node_embed = torch.spmm(self.node_features, self.w_n2l)
else:
node_embed = torch.mm(self.node_features, self.w_n2l)
node_embed += self.bias_n2l
input_message = node_embed
node_embed = F.relu(input_message)
for i in range(self.max_lv):
n2npool = torch.spmm(adj, node_embed)
node_linear = self.conv_params(n2npool)
merged_linear = node_linear + input_message
node_embed = F.relu(merged_linear)
graph_embed = torch.mean(node_embed, dim=0, keepdim=True)
return graph_embed, node_embed
def make_spmat(self, n_rows, n_cols, row_idx, col_idx):
idxes = torch.LongTensor([[row_idx], [col_idx]])
values = torch.ones(1)
sp = torch.sparse.FloatTensor(idxes, values, torch.Size([n_rows, n_cols]))
if next(self.parameters()).is_cuda:
sp = sp.cuda()
return sp
def forward(self, time_t, states, actions, greedy_acts=False, is_inference=False):
preds = torch.zeros(len(states)).to(self.device)
batch_graph, modified_labels = zip(*states)
greedy_actions = []
with torch.set_grad_enabled(mode=not is_inference):
for i in range(len(batch_graph)):
if batch_graph[i] is None:
continue
adj = self.norm_tool.norm_extra(batch_graph[i].get_extra_adj(self.device))
# get graph representation
graph_embed, node_embed = self.get_graph_embedding(adj)
# get label reprensentation
label_embed = self.get_label_embedding(modified_labels[i])
# get action reprensentation
if time_t != 2:
action_embed = node_embed[actions[i]].view(-1, self.embed_dim)
else:
action_embed = self.get_action_label_encoding(actions[i])
# concat them and send it to neural network
embed_s = torch.cat((graph_embed, label_embed), dim=1)
embed_s = embed_s.repeat(len(action_embed), 1)
embed_s_a = torch.cat((embed_s, action_embed), dim=1)
if self.mlp_hidden:
embed_s_a = F.relu( self.linear_1(embed_s_a) )
raw_pred = self.linear_out(embed_s_a)
if greedy_acts:
action_id = raw_pred.argmax(0)
raw_pred = raw_pred.max()
greedy_actions.append(actions[i][action_id])
else:
raw_pred = raw_pred.max()
# list_pred.append(raw_pred)
preds[i] += raw_pred
return greedy_actions, preds
class NStepQNetNode(nn.Module):
def __init__(self, num_steps, node_features, node_labels, list_action_space, n_injected, bilin_q=1, embed_dim=64, mlp_hidden=64, max_lv=1, gm='mean_field', device='cpu'):
super(NStepQNetNode, self).__init__()
self.node_features = node_features
self.node_labels = node_labels
self.list_action_space = list_action_space
self.total_nodes = len(list_action_space)
list_mod = []
for i in range(0, num_steps):
# list_mod.append(QNetNode(node_features, node_labels, list_action_space))
list_mod.append(QNetNode(node_features, node_labels, list_action_space, n_injected, bilin_q, embed_dim, mlp_hidden, max_lv, gm=gm, device=device))
self.list_mod = nn.ModuleList(list_mod)
self.num_steps = num_steps
def forward(self, time_t, states, actions, greedy_acts = False, is_inference=False):
# print('time_t:', time_t)
# print('self.num_step:', self.num_steps)
# assert time_t >= 0 and time_t < self.num_steps
time_t = time_t % 3
return self.list_mod[time_t](time_t, states, actions, greedy_acts, is_inference)
def glorot_uniform(t):
if len(t.size()) == 2:
fan_in, fan_out = t.size()
elif len(t.size()) == 3:
# out_ch, in_ch, kernel for Conv 1
fan_in = t.size()[1] * t.size()[2]
fan_out = t.size()[0] * t.size()[2]
else:
fan_in = np.prod(t.size())
fan_out = np.prod(t.size())
limit = np.sqrt(6.0 / (fan_in + fan_out))
t.uniform_(-limit, limit)
def _param_init(m):
if isinstance(m, Parameter):
glorot_uniform(m.data)
elif isinstance(m, nn.Linear):
m.bias.data.zero_()
glorot_uniform(m.weight.data)
def weights_init(m):
for p in m.modules():
if isinstance(p, nn.ParameterList):
for pp in p:
_param_init(pp)
else:
_param_init(p)
for name, p in m.named_parameters():
if not '.' in name: # top-level parameters
_param_init(p)
def node_greedy_actions(target_nodes, picked_nodes, list_q, net):
assert len(target_nodes) == len(list_q)
actions = []
values = []
for i in range(len(target_nodes)):
region = net.list_action_space[target_nodes[i]]
if picked_nodes is not None and picked_nodes[i] is not None:
region = net.list_action_space[picked_nodes[i]]
if region is None:
assert list_q[i].size()[0] == net.total_nodes
else:
assert len(region) == list_q[i].size()[0]
val, act = torch.max(list_q[i], dim=0)
values.append(val)
if region is not None:
act = region[act.data.cpu().numpy()[0]]
# act = Variable(torch.LongTensor([act]))
act = torch.LongTensor([act])
actions.append(act)
else:
actions.append(act)
return torch.cat(actions, dim=0).data, torch.cat(values, dim=0).data
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