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models.py
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import torch
import torch.nn as nn
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import GATConv
from torch_geometric.nn.conv.sage_conv import SAGEConv
from torch_geometric.data import Data
from torch_geometric.utils import add_self_loops, degree
import torch.nn.functional as F
import numpy as np
class LowLayer(nn.Module):
def __init__(self, in_feats, out_feats):
super(LowLayer, self).__init__()
self.linear = nn.Linear(in_feats, out_feats)
self.init_params()
def init_params(self):
for param in self.parameters():
if len(param.size()) == 2:
nn.init.kaiming_normal_(param)
def forward(self, x):
x = self.linear(x)
x = F.relu(x)
return x
class GNNLayer(torch.nn.Module):
def __init__(self, sageMode, in_feats, out_feats, h_feats, linear, layer_size=2):
super(GNNLayer, self).__init__()
self.layer_size = layer_size
self.sageMode = sageMode
self.linear = linear
if self.sageMode == "GraphSAGE":
self.sage1 = SAGEConv(in_feats, h_feats)
self.sage2 = SAGEConv(h_feats, out_feats)
self.sagex = [SAGEConv(h_feats, h_feats)
for layer in range(layer_size - 2)]
elif self.sageMode == "GAT":
self.sage1 = GATConv(in_feats, h_feats, dropout=0.5)
self.sage2 = GATConv(h_feats, out_feats, dropout=0.5)
self.sagex = [GATConv(h_feats, h_feats, dropout=0.5)
for layer in range(layer_size - 2)]
self.init_params()
def init_params(self):
for param in self.parameters():
if len(param.size()) == 2:
nn.init.kaiming_normal_(param)
def forward(self, x, edge_index):
x = self.sage1(x, edge_index)
if not self.linear:
x = F.relu(x)
for layer in range(self.layer_size - 2):
x = self.sagex[layer](x, edge_index)
if not self.linear:
x = F.relu(x)
x = self.sage2(x, edge_index)
x = F.relu(x)
return x
class Classification(nn.Module):
def __init__(self, in_feats, num_classes):
super(Classification, self).__init__()
self.weight = nn.Parameter(torch.FloatTensor(num_classes, in_feats))
self.init_params()
def init_params(self):
for param in self.parameters():
if len(param.size()) == 2:
nn.init.kaiming_normal_(param)
def forward(self, embeds):
logists = torch.log_softmax(self.weight.mm(embeds.t()).t(), 1)
return logists
class FedGCN(torch.nn.Module):
def __init__(self, in_feats, h_feats, out_feats):
super(FedGCN, self).__init__()
self.linear1 = nn.Linear(in_feats, h_feats)
self.linear2 = nn.Linear(h_feats, out_feats)
self.init_params()
def init_params(self):
for param in self.parameters():
if len(param.size()) == 2:
nn.init.kaiming_normal_(param)
def forward(self, x, adj):
x = self.linear1(x)
x = adj.mm(x)
x = F.relu(x)
x = F.dropout(x, p=0.5)
x = adj.mm(x)
x = self.linear2(x)
x = F.relu(x)
x = F.softmax(x, dim=1)
return x
class Sampling(nn.Module):
def __init__(self):
super(Sampling, self).__init__()
def forward(self, inputs):
rand = torch.normal(0, 1, size=inputs.shape)
return inputs + rand.to(inputs.device)
class FeatGenerator(nn.Module):
def __init__(self, latent_dim, dropout, num_pred, feat_shape):
super(FeatGenerator, self).__init__()
self.num_pred = num_pred
self.feat_shape = feat_shape
self.dropout = dropout
self.sample = Sampling()
self.fc1 = nn.Linear(latent_dim, 256)
self.fc2 = nn.Linear(256, 2048)
self.fc_flat = nn.Linear(2048, self.num_pred * self.feat_shape)
def forward(self, x):
x = self.sample(x)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.dropout(x, self.dropout, training=self.training)
x = torch.tanh(self.fc_flat(x))
return x
class NumPredictor(nn.Module):
def __init__(self, latent_dim):
self.latent_dim = latent_dim
super(NumPredictor, self).__init__()
self.reg_1 = nn.Linear(self.latent_dim, 1)
def forward(self, x):
x = F.relu(self.reg_1(x))
return x
# Mend the graph via NeighGen
class MendGraph(nn.Module):
def __init__(self, num_pred):
super(MendGraph, self).__init__()
self.num_pred = num_pred
for param in self.parameters():
param.requires_grad = False
def mend_graph(self, x, edge_index, pred_degree, gen_feats):
device = gen_feats.device
num_node, num_feature = x.shape
new_edges = []
gen_feats = gen_feats.view(-1, self.num_pred, num_feature)
if pred_degree.device.type != 'cpu':
pred_degree = pred_degree.cpu()
pred_degree = torch._cast_Int(torch.round(pred_degree)).detach()
x = x.detach()
fill_feats = torch.vstack((x, gen_feats.view(-1, num_feature)))
for i in range(num_node):
for j in range(min(self.num_pred, max(0, pred_degree[i]))):
new_edges.append(
np.asarray([i, num_node + i * self.num_pred + j]))
new_edges = torch.tensor(np.asarray(new_edges).reshape((-1, 2)),
dtype=torch.int64).T
new_edges = new_edges.to(device)
if len(new_edges) > 0:
fill_edges = torch.hstack((edge_index, new_edges))
else:
fill_edges = torch.clone(edge_index)
return fill_feats, fill_edges
def forward(self, x, edge_index, pred_missing, gen_feats):
fill_feats, fill_edges = self.mend_graph(x, edge_index, pred_missing,
gen_feats)
return fill_feats, fill_edges
class LocalSage_Plus(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden,
gen_hidden,
linear,
dropout=0.5,
num_pred=5):
super(LocalSage_Plus, self).__init__()
self.encoder_model = GNNLayer(sageMode="GraphSAGE",
in_feats=in_channels,
out_feats=gen_hidden,
h_feats=hidden,
linear=linear,
layer_size=2)
self.reg_model = NumPredictor(latent_dim=gen_hidden)
self.gen = FeatGenerator(latent_dim=gen_hidden,
dropout=dropout,
num_pred=num_pred,
feat_shape=in_channels)
self.mend_graph = MendGraph(num_pred)
self.classifier = GNNLayer(sageMode="GraphSAGE",
in_feats=in_channels,
out_feats=out_channels,
h_feats=hidden,
linear=linear,
layer_size=2)
def forward(self, data):
x = self.encoder_model(data.x, data.edge_index)
degree = self.reg_model(x)
gen_feat = self.gen(x)
mend_feats, mend_edge_index = self.mend_graph(data.x, data.edge_index,
degree, gen_feat)
nc_pred = self.classifier(mend_feats, mend_edge_index)
return degree, gen_feat, nc_pred[:data.num_nodes]
def inference(self, impared_data, raw_data):
x = self.encoder_model(impared_data.x, impared_data.edge_index)
degree = self.reg_model(x)
gen_feat = self.gen(x)
mend_feats, mend_edge_index = self.mend_graph(raw_data.x,
raw_data.edge_index,
degree, gen_feat)
nc_pred = self.classifier(mend_feats, mend_edge_index)
return degree, gen_feat, nc_pred[:raw_data.num_nodes]
class FedSage_Plus(nn.Module):
def __init__(self, local_graph: LocalSage_Plus):
super(FedSage_Plus, self).__init__()
self.encoder_model = local_graph.encoder_model
self.reg_model = local_graph.reg_model
self.gen = local_graph.gen
self.mend_graph = local_graph.mend_graph
self.classifier = local_graph.classifier
self.encoder_model.requires_grad_(False)
self.reg_model.requires_grad_(False)
self.mend_graph.requires_grad_(False)
self.classifier.requires_grad_(False)
def forward(self, data):
x = self.encoder_model(data.x, data.edge_index)
degree = self.reg_model(x)
gen_feat = self.gen(x)
mend_feats, mend_edge_index = self.mend_graph(data.x, data.edge_index,
degree, gen_feat)
nc_pred = self.classifier(mend_feats, mend_edge_index)
return degree, gen_feat, nc_pred[:data.num_nodes]