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import torch
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool, GCNConv, GINConv, ChebConv
from ogb.graphproppred.mol_encoder import AtomEncoder
from .conv import GCNConvNew, GINConvNew, ChebConvNew
Cheb_K = 3
# mol
class GNN(torch.nn.Module):
"""
Graph Isomorphism Network augmented with virtual node for multi-task binary graph classification
Input:
- batched Pytorch Geometric graph object
Output:
- prediction (Tensor): float torch tensor of shape (num_graphs, num_tasks)
"""
def __init__(self, gnn_type, dataset_group, num_tasks=128, num_layers=5, emb_dim=300, dropout=0.5, is_pooled=True,
**model_kwargs):
"""
Args:
- num_tasks (int): number of binary label tasks. default to 128 (number of tasks of ogbg-molpcba)
- num_layers (int): number of message passing layers of GNN
- emb_dim (int): dimensionality of hidden channels
- dropout (float): dropout ratio applied to hidden channels
"""
self.gnn_type = gnn_type
self.dataset_group = dataset_group
super(GNN, self).__init__()
if self.gnn_type.endswith('layers') :
num_layers = int(self.gnn_type.split('_')[1])
residual = True
else :
residual = False
self.num_layers = num_layers
self.dropout = dropout
self.emb_dim = emb_dim
self.num_tasks = num_tasks
self.is_pooled = is_pooled
if num_tasks is None:
self.d_out = self.emb_dim
else:
self.d_out = self.num_tasks
if self.num_layers < 2:
raise ValueError("Number of GNN layers must be greater than 1.")
if self.gnn_type.endswith('virtual'):
self.gnn_node = GNN_node_Virtualnode(num_layers, emb_dim, dataset_group=self.dataset_group,
gnn_type=self.gnn_type.split('_')[0], dropout=dropout,
residual=residual)
else:
self.gnn_node = GNN_node(num_layers, emb_dim, dataset_group=self.dataset_group,
gnn_type=self.gnn_type.split('_')[0], dropout=dropout,
residual=residual)
# Pooling function to generate whole-graph embeddings
if self.is_pooled:
self.pool = global_mean_pool
else:
self.pool = None
if num_tasks is None:
self.graph_pred_linear = None
else:
assert self.pool is not None
self.graph_pred_linear = torch.nn.Linear(self.emb_dim, self.num_tasks)
def forward(self, batched_data, perturb=None):
h_node = self.gnn_node(batched_data, perturb)
if self.graph_pred_linear is None:
if self.pool is None:
return h_node, batched_data.batch
else:
return self.pool(h_node, batched_data.batch)
else:
return self.graph_pred_linear(self.pool(h_node, batched_data.batch))
### GNN to generate node embedding
class GNN_node(torch.nn.Module):
"""
Output:
node representations
"""
def __init__(self, num_layer, emb_dim, dataset_group='mol', gnn_type='gin', dropout=0.5, JK="last",
residual=False, batchnorm=True):
'''
emb_dim (int): node embedding dimensionality
num_layer (int): number of GNN message passing layers
'''
super(GNN_node, self).__init__()
self.dataset_group = dataset_group
self.gnn_type = gnn_type
self.num_layer = num_layer
self.drop_ratio = dropout
self.JK = JK
### add residual connection or not
self.residual = residual
self.batchnorm = batchnorm
if self.num_layer < 2:
raise ValueError("Number of GNN layers must be greater than 1.")
if self.dataset_group == 'mol':
self.node_encoder = AtomEncoder(emb_dim)
elif self.dataset_group == 'ppa':
self.node_encoder = torch.nn.Embedding(1, emb_dim) # uniform input node embedding
elif self.dataset_group == 'RotatedMNIST':
self.node_encoder = torch.nn.Linear(1, emb_dim)
elif self.dataset_group == 'ColoredMNIST' :
self.node_encoder = torch.nn.Linear(2, emb_dim)
# self.node_encoder_cate = torch.nn.Embedding(8, emb_dim)
elif self.dataset_group == 'SBM' :
self.node_encoder = torch.nn.Embedding(8, emb_dim)
elif self.dataset_group == 'UPFD':
self.node_encoder = torch.nn.Embedding(8, emb_dim)
else:
raise NotImplementedError
###List of GNNs
self.convs = torch.nn.ModuleList()
self.batch_norms = torch.nn.ModuleList()
for layer in range(num_layer):
if gnn_type == 'gin':
if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
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.convs.append(GINConv(mlp, train_eps=True))
else:
self.convs.append(GINConvNew(emb_dim, self.dataset_group))
elif gnn_type == 'gcn':
if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
self.convs.append(GCNConv(emb_dim, emb_dim))
else:
self.convs.append(GCNConvNew(emb_dim, self.dataset_group))
elif gnn_type == 'cheb' :
if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
self.convs.append(ChebConv(emb_dim, emb_dim, Cheb_K))
else:
self.convs.append(ChebConvNew(emb_dim, Cheb_K, self.dataset_group))
else:
raise ValueError('Undefined GNN type called {}'.format(gnn_type))
self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))
def destroy_node_encoder(self):
self.node_encoder = None
def forward(self, batched_data, perturb=None):
x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch
# FLAG injects perturbation
if self.node_encoder is None:
h_list = [x]
else:
# if self.dataset_group == 'ColoredMNIST' :
# h_list = [self.node_encoder(x[:,:2]) + self.node_encoder_cate(x[:,2:].to(torch.int).squeeze()) + perturb if perturb is not None else self.node_encoder(x[:,:2]) + self.node_encoder_cate(x[:,2:].to(torch.int).squeeze())]
# else :
h_list = [self.node_encoder(x) + perturb if perturb is not None else self.node_encoder(x)]
for layer in range(self.num_layer):
h = self.convs[layer](h_list[layer], edge_index, edge_attr)
if self.batchnorm :
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 + 1):
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, dataset_group='mol', gnn_type='gin', dropout=0.5, JK="last",
residual=False, batchnorm=True):
'''
emb_dim (int): node embedding dimensionality
'''
super(GNN_node_Virtualnode, self).__init__()
self.dataset_group = dataset_group
self.gnn_type = gnn_type
self.num_layer = num_layer
self.drop_ratio = dropout
self.JK = JK
### add residual connection or not
self.residual = residual
self.batchnorm = batchnorm
if self.num_layer < 2:
raise ValueError("Number of GNN layers must be greater than 1.")
if self.dataset_group == 'mol':
self.node_encoder = AtomEncoder(emb_dim)
elif self.dataset_group == 'ppa':
self.node_encoder = torch.nn.Embedding(1, emb_dim) # uniform input node embedding
elif self.dataset_group == 'RotatedMNIST':
self.node_encoder = torch.nn.Linear(1, emb_dim)
elif self.dataset_group == 'ColoredMNIST' :
self.node_encoder = torch.nn.Linear(2, emb_dim)
# self.node_encoder_cate = torch.nn.Embedding(8, emb_dim)
elif self.dataset_group == 'SBM' :
self.node_encoder = torch.nn.Embedding(8, emb_dim)
elif self.dataset_group == 'UPFD':
self.node_encoder = torch.nn.Embedding(8, emb_dim)
else:
raise NotImplementedError
### 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':
if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
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.convs.append(GINConv(mlp, train_eps=True))
else:
self.convs.append(GINConvNew(emb_dim, self.dataset_group))
elif gnn_type == 'gcn':
if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
self.convs.append(GCNConv(emb_dim, emb_dim))
else:
self.convs.append(GCNConvNew(emb_dim, self.dataset_group))
elif gnn_type == 'cheb' :
if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
self.convs.append(ChebConv(emb_dim, emb_dim, Cheb_K))
else:
self.convs.append(ChebConvNew(emb_dim, Cheb_K, self.dataset_group))
else:
raise 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 destroy_node_encoder(self):
self.node_encoder = None
def forward(self, batched_data, perturb=None):
x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, 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))
# FLAG injects perturbation
if self.node_encoder is None:
h_list = [x]
else:
# if self.dataset_group == 'ColoredMNIST' :
# h_list = [self.node_encoder(x[:,:2]) + self.node_encoder_cate(x[:,2:].to(torch.int).squeeze()) + perturb if perturb is not None else self.node_encoder(x[:,:2]) + self.node_encoder_cate(x[:,2:].to(torch.int).squeeze())]
# else :
h_list = [self.node_encoder(x) + perturb if perturb is not None else self.node_encoder(x)]
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)
if self.batchnorm:
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 + 1):
node_representation += h_list[layer]
return node_representation
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