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gnn_models_sag.py
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gnn_models_sag.py
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import torch
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import random
# from pygcn.layers import GraphConvolution
# from dgl.nn import GraphConv, EdgeWeightNorm
from torch_geometric.nn import GINConv, JumpingKnowledge, global_mean_pool, SAGEConv, GCNConv
from torch_geometric.nn.pool import SAGPooling
from torch_geometric.nn import global_mean_pool
class GIN(torch.nn.Module):
def __init__(self, hidden=512, train_eps=True, class_num=7):
super(GIN, self).__init__()
self.train_eps = train_eps
self.gin_conv1 = GINConv(
nn.Sequential(
nn.Linear(128, hidden),
nn.ReLU(),
nn.Linear(hidden, hidden),
nn.ReLU(),
# nn.Linear(hidden, hidden),
# nn.ReLU(),
nn.BatchNorm1d(hidden),
), train_eps=self.train_eps
)
self.gin_conv2 = GINConv(
nn.Sequential(
nn.Linear(hidden, hidden),
nn.ReLU(),
# nn.Linear(hidden, hidden),
# nn.ReLU(),
nn.BatchNorm1d(hidden),
), train_eps=self.train_eps
)
self.gin_conv3 = GINConv(
nn.Sequential(
nn.Linear(hidden, hidden),
nn.ReLU(),
nn.Linear(hidden, hidden),
nn.ReLU(),
nn.BatchNorm1d(hidden),
), train_eps=self.train_eps
)
self.lin1 = nn.Linear(hidden, hidden)
self.fc1 = nn.Linear(2 * hidden, 7) #clasifier for concat
self.fc2 = nn.Linear(hidden, 7) #classifier for inner product
def reset_parameters(self):
self.fc1.reset_parameters()
self.gin_conv1.reset_parameters()
self.gin_conv2.reset_parameters()
# self.gin_conv3.reset_parameters()
self.lin1.reset_parameters()
self.fc1.reset_parameters()
self.fc2.reset_parameters()
def forward(self, x, edge_index, train_edge_id, p=0.5):
x = self.gin_conv1(x, edge_index)
x = self.gin_conv2(x, edge_index)
# x = self.gin_conv3(x, edge_index)
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
node_id = edge_index[:, train_edge_id]
x1 = x[node_id[0]]
x2 = x[node_id[1]]
# x = torch.cat([x1, x2], dim=1)
# x = self.fc1(x)
x = torch.mul(x1, x2)
x = self.fc2(x)
return x
class GCN(nn.Module):
def __init__(self):
super(GCN, self).__init__()
hidden = 128
self.conv1 = GCNConv(7, hidden)
self.conv2 = GCNConv(hidden, hidden)
self.conv3 = GCNConv(hidden, hidden)
self.conv4 = GCNConv(hidden, hidden)
self.bn1 = nn.BatchNorm1d(hidden)
self.bn2 = nn.BatchNorm1d(hidden)
self.bn3 = nn.BatchNorm1d(hidden)
self.bn4 = nn.BatchNorm1d(hidden)
self.sag1 = SAGPooling(hidden,0.5)
self.sag2 = SAGPooling(hidden,0.5)
self.sag3 = SAGPooling(hidden,0.5)
self.sag4 = SAGPooling(hidden,0.5)
self.fc1 = nn.Linear(hidden, hidden)
self.fc2 = nn.Linear(hidden, hidden)
self.fc3 = nn.Linear(hidden, hidden)
self.fc4 = nn.Linear(hidden, hidden)
self.dropout = nn.Dropout(0.5)
for param in self.parameters():
print(type(param), param.size())
def forward(self, x, edge_index, batch):
x = self.conv1(x, edge_index)
x = self.fc1(x)
x = F.relu(x)
x = self.bn1(x)
y = self.sag1(x, edge_index, batch = batch)
x = y[0]
batch = y[3]
edge_index = y[1]
x = self.conv2(x, edge_index)
x = self.fc2(x)
x = F.relu(x)
x = self.bn2(x)
y = self.sag2(x, edge_index, batch = batch)
x = y[0]
batch = y[3]
edge_index = y[1]
x = self.conv3(x, edge_index)
x = self.fc3(x)
x = F.relu(x)
x = self.bn3(x)
y = self.sag3(x, edge_index, batch = batch)
x = y[0]
batch = y[3]
edge_index = y[1]
x = self.conv4(x, edge_index)
x = self.fc4(x)
x = F.relu(x)
x = self.bn4(x)
y = self.sag4(x, edge_index, batch = batch)
x = y[0]
batch = y[3]
edge_index = y[1]
# y = self.sag4(x, edge_index, batch = batch)
return global_mean_pool(y[0], y[3])
# return y[0]
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
class ppi_model(nn.Module):
def __init__(self):
super(ppi_model,self).__init__()
self.BGNN = GCN()
self.TGNN = GIN()
def forward(self, batch, p_x_all, p_edge_all, edge_index, train_edge_id, p=0.5):
edge_index = edge_index.to(device)
batch = batch.to(torch.int64).to(device)
x = p_x_all.to(torch.float32).to(device)
edge = torch.LongTensor(p_edge_all.to(torch.int64)).to(device)
embs = self.BGNN(x, edge, batch-1)
final = self.TGNN(embs, edge_index, train_edge_id, p=0.5)
return final