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import torch
from torch_geometric.nn import MessagePassing
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool
from torch_geometric.utils import degree
import math
### GIN convolution along the graph structure
class GINConv(MessagePassing):
def __init__(self, emb_dim, edge_attr_dim):
'''
emb_dim (int): node embedding dimensionality
'''
super(GINConv, self).__init__(aggr = "add")
self.mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), torch.nn.ReLU(), torch.nn.Linear(2*emb_dim, emb_dim))
self.eps = torch.nn.Parameter(torch.Tensor([0]))
# edge_attr is two dimensional after augment_edge transformation
self.edge_encoder = torch.nn.Linear(edge_attr_dim, emb_dim)
def forward(self, x, edge_index, edge_attr):
edge_embedding = self.edge_encoder(edge_attr)
out = self.mlp((1 + self.eps) *x + self.propagate(edge_index, x=x, edge_attr=edge_embedding))
return out
def message(self, x_j, edge_attr):
return F.relu(x_j + edge_attr)
def update(self, aggr_out):
return aggr_out
### GCN convolution along the graph structure
class GCNConv(MessagePassing):
def __init__(self, emb_dim, edge_attr_dim):
super(GCNConv, self).__init__(aggr='add')
self.linear = torch.nn.Linear(emb_dim, emb_dim)
self.root_emb = torch.nn.Embedding(1, emb_dim)
# edge_attr is two dimensional after augment_edge transformation
self.edge_encoder = torch.nn.Linear(edge_attr_dim, emb_dim)
def forward(self, x, edge_index, edge_attr):
x = self.linear(x)
edge_embedding = self.edge_encoder(edge_attr)
row, col = edge_index
#edge_weight = torch.ones((edge_index.size(1), ), device=edge_index.device)
deg = degree(row, x.size(0), dtype = x.dtype) + 1
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
return self.propagate(edge_index, x=x, edge_attr = edge_embedding, norm=norm) + F.relu(x + self.root_emb.weight) * 1./deg.view(-1,1)
def message(self, x_j, edge_attr, norm):
return norm.view(-1, 1) * F.relu(x_j + edge_attr)
def update(self, aggr_out):
return aggr_out
### GNN to generate node embedding
class GNN_node(torch.nn.Module):
"""
Output:
node representations
"""
def __init__(self, num_layer, emb_dim, node_encoder, drop_ratio = 0.5, JK = "last", residual = False, gnn_type = 'gin', edge_attr_dim=2):
'''
emb_dim (int): node embedding dimensionality
num_layer (int): number of GNN message passing layers
'''
super(GNN_node, self).__init__()
self.num_layer = num_layer
self.drop_ratio = drop_ratio
self.JK = JK
### add residual connection or not
self.residual = residual
if self.num_layer < 2:
raise ValueError("Number of GNN layers must be greater than 1.")
self.node_encoder = node_encoder
###List of GNNs
self.convs = torch.nn.ModuleList()
self.batch_norms = torch.nn.ModuleList()
for layer in range(num_layer):
if gnn_type == 'gin':
self.convs.append(GINConv(emb_dim, edge_attr_dim))
elif gnn_type == 'gcn':
self.convs.append(GCNConv(emb_dim, edge_attr_dim))
else:
ValueError('Undefined GNN type called {}'.format(gnn_type))
self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))
def forward(self, batched_data):
x, edge_index, edge_attr, node_depth, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.node_depth, batched_data.batch
### computing input node embedding
h_list = [self.node_encoder(x, node_depth.view(-1,))]
for layer in range(self.num_layer):
h = self.convs[layer](h_list[layer], edge_index, edge_attr)
h = self.batch_norms[layer](h)
if layer == self.num_layer - 1:
#remove relu for the last layer
h = F.dropout(h, self.drop_ratio, training = self.training)
else:
h = F.dropout(F.relu(h), self.drop_ratio, training = self.training)
if self.residual:
h += h_list[layer]
h_list.append(h)
### Different implementations of Jk-concat
if self.JK == "last":
node_representation = h_list[-1]
elif self.JK == "sum":
node_representation = 0
for layer in range(self.num_layer):
node_representation += h_list[layer]
return node_representation
### Virtual GNN to generate node embedding
class GNN_node_Virtualnode(torch.nn.Module):
"""
Output:
node representations
"""
def __init__(self, num_layer, emb_dim, node_encoder, drop_ratio = 0.5, JK = "last", residual = False, gnn_type = 'gin', edge_attr_dim=2):
'''
emb_dim (int): node embedding dimensionality
'''
super(GNN_node_Virtualnode, self).__init__()
self.num_layer = num_layer
self.drop_ratio = drop_ratio
self.JK = JK
### add residual connection or not
self.residual = residual
if self.num_layer < 2:
raise ValueError("Number of GNN layers must be greater than 1.")
self.node_encoder = node_encoder
### set the initial virtual node embedding to 0.
self.virtualnode_embedding = torch.nn.Embedding(1, emb_dim)
torch.nn.init.constant_(self.virtualnode_embedding.weight.data, 0)
### List of GNNs
self.convs = torch.nn.ModuleList()
### batch norms applied to node embeddings
self.batch_norms = torch.nn.ModuleList()
### List of MLPs to transform virtual node at every layer
self.mlp_virtualnode_list = torch.nn.ModuleList()
for layer in range(num_layer):
if gnn_type == 'gin':
self.convs.append(GINConv(emb_dim, edge_attr_dim))
elif gnn_type == 'gcn':
self.convs.append(GCNConv(emb_dim, edge_attr_dim))
else:
ValueError('Undefined GNN type called {}'.format(gnn_type))
self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))
for layer in range(num_layer - 1):
self.mlp_virtualnode_list.append(torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), torch.nn.ReLU(), \
torch.nn.Linear(2*emb_dim, emb_dim), torch.nn.BatchNorm1d(emb_dim), torch.nn.ReLU()))
def forward(self, batched_data):
x, edge_index, edge_attr, node_depth, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.node_depth, batched_data.batch
### virtual node embeddings for graphs
virtualnode_embedding = self.virtualnode_embedding(torch.zeros(batch[-1].item() + 1).to(edge_index.dtype).to(edge_index.device))
h_list = [self.node_encoder(x, node_depth.view(-1,))]
for layer in range(self.num_layer):
### add message from virtual nodes to graph nodes
h_list[layer] = h_list[layer] + virtualnode_embedding[batch]
### Message passing among graph nodes
h = self.convs[layer](h_list[layer], edge_index, edge_attr)
h = self.batch_norms[layer](h)
if layer == self.num_layer - 1:
#remove relu for the last layer
h = F.dropout(h, self.drop_ratio, training = self.training)
else:
h = F.dropout(F.relu(h), self.drop_ratio, training = self.training)
if self.residual:
h = h + h_list[layer]
h_list.append(h)
### update the virtual nodes
if layer < self.num_layer - 1:
### add message from graph nodes to virtual nodes
virtualnode_embedding_temp = global_add_pool(h_list[layer], batch) + virtualnode_embedding
### transform virtual nodes using MLP
if self.residual:
virtualnode_embedding = virtualnode_embedding + F.dropout(self.mlp_virtualnode_list[layer](virtualnode_embedding_temp), self.drop_ratio, training = self.training)
else:
virtualnode_embedding = F.dropout(self.mlp_virtualnode_list[layer](virtualnode_embedding_temp), self.drop_ratio, training = self.training)
### Different implementations of Jk-concat
if self.JK == "last":
node_representation = h_list[-1]
elif self.JK == "sum":
node_representation = 0
for layer in range(self.num_layer):
node_representation += h_list[layer]
return node_representation
if __name__ == "__main__":
pass |