Source code for deeprobust.graph.rl.nipa_q_net_node

'''
    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

[docs]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
[docs]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