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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import GraphConv, SAGEConv, SortPooling, SumPooling
class GCN(nn.Module):
"""
GCN Model
Attributes:
num_layers(int): num of gcn layers
hidden_units(int): num of hidden units
gcn_type(str): type of gcn layer, 'gcn' for GraphConv and 'sage' for SAGEConv
pooling_type(str): type of graph pooling to get subgraph representation
'sum' for sum pooling and 'center' for center pooling.
node_attributes(Tensor, optional): node attribute
edge_weights(Tensor, optional): edge weight
node_embedding(Tensor, optional): pre-trained node embedding
use_embedding(bool, optional): whether to use node embedding. Note that if 'use_embedding' is set True
and 'node_embedding' is None, will automatically randomly initialize node embedding.
num_nodes(int, optional): num of nodes
dropout(float, optional): dropout rate
max_z(int, optional): default max vocab size of node labeling, default 1000.
"""
def __init__(
self,
num_layers,
hidden_units,
gcn_type="gcn",
pooling_type="sum",
node_attributes=None,
edge_weights=None,
node_embedding=None,
use_embedding=False,
num_nodes=None,
dropout=0.5,
max_z=1000,
):
super(GCN, self).__init__()
self.num_layers = num_layers
self.dropout = dropout
self.pooling_type = pooling_type
self.use_attribute = False if node_attributes is None else True
self.use_embedding = use_embedding
self.use_edge_weight = False if edge_weights is None else True
self.z_embedding = nn.Embedding(max_z, hidden_units)
if node_attributes is not None:
self.node_attributes_lookup = nn.Embedding.from_pretrained(
node_attributes
)
self.node_attributes_lookup.weight.requires_grad = False
if edge_weights is not None:
self.edge_weights_lookup = nn.Embedding.from_pretrained(
edge_weights
)
self.edge_weights_lookup.weight.requires_grad = False
if node_embedding is not None:
self.node_embedding = nn.Embedding.from_pretrained(node_embedding)
self.node_embedding.weight.requires_grad = False
elif use_embedding:
self.node_embedding = nn.Embedding(num_nodes, hidden_units)
initial_dim = hidden_units
if self.use_attribute:
initial_dim += self.node_attributes_lookup.embedding_dim
if self.use_embedding:
initial_dim += self.node_embedding.embedding_dim
self.layers = nn.ModuleList()
if gcn_type == "gcn":
self.layers.append(
GraphConv(initial_dim, hidden_units, allow_zero_in_degree=True)
)
for _ in range(num_layers - 1):
self.layers.append(
GraphConv(
hidden_units, hidden_units, allow_zero_in_degree=True
)
)
elif gcn_type == "sage":
self.layers.append(
SAGEConv(initial_dim, hidden_units, aggregator_type="gcn")
)
for _ in range(num_layers - 1):
self.layers.append(
SAGEConv(hidden_units, hidden_units, aggregator_type="gcn")
)
else:
raise ValueError("Gcn type error.")
self.linear_1 = nn.Linear(hidden_units, hidden_units)
self.linear_2 = nn.Linear(hidden_units, 1)
if pooling_type != "sum":
raise ValueError("Pooling type error.")
self.pooling = SumPooling()
def reset_parameters(self):
for layer in self.layers:
layer.reset_parameters()
def forward(self, g, z, node_id=None, edge_id=None):
"""
Args:
g(DGLGraph): the graph
z(Tensor): node labeling tensor, shape [N, 1]
node_id(Tensor, optional): node id tensor, shape [N, 1]
edge_id(Tensor, optional): edge id tensor, shape [E, 1]
Returns:
x(Tensor): output tensor
"""
z_emb = self.z_embedding(z)
if self.use_attribute:
x = self.node_attributes_lookup(node_id)
x = torch.cat([z_emb, x], 1)
else:
x = z_emb
if self.use_edge_weight:
edge_weight = self.edge_weights_lookup(edge_id)
else:
edge_weight = None
if self.use_embedding:
n_emb = self.node_embedding(node_id)
x = torch.cat([x, n_emb], 1)
for layer in self.layers[:-1]:
x = layer(g, x, edge_weight=edge_weight)
x = F.relu(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.layers[-1](g, x, edge_weight=edge_weight)
x = self.pooling(g, x)
x = F.relu(self.linear_1(x))
F.dropout(x, p=self.dropout, training=self.training)
x = self.linear_2(x)
return x
class DGCNN(nn.Module):
"""
An end-to-end deep learning architecture for graph classification.
paper link: https://muhanzhang.github.io/papers/AAAI_2018_DGCNN.pdf
Attributes:
num_layers(int): num of gcn layers
hidden_units(int): num of hidden units
k(int, optional): The number of nodes to hold for each graph in SortPooling.
gcn_type(str): type of gcn layer, 'gcn' for GraphConv and 'sage' for SAGEConv
node_attributes(Tensor, optional): node attribute
edge_weights(Tensor, optional): edge weight
node_embedding(Tensor, optional): pre-trained node embedding
use_embedding(bool, optional): whether to use node embedding. Note that if 'use_embedding' is set True
and 'node_embedding' is None, will automatically randomly initialize node embedding.
num_nodes(int, optional): num of nodes
dropout(float, optional): dropout rate
max_z(int, optional): default max vocab size of node labeling, default 1000.
"""
def __init__(
self,
num_layers,
hidden_units,
k=10,
gcn_type="gcn",
node_attributes=None,
edge_weights=None,
node_embedding=None,
use_embedding=False,
num_nodes=None,
dropout=0.5,
max_z=1000,
):
super(DGCNN, self).__init__()
self.num_layers = num_layers
self.dropout = dropout
self.use_attribute = False if node_attributes is None else True
self.use_embedding = use_embedding
self.use_edge_weight = False if edge_weights is None else True
self.z_embedding = nn.Embedding(max_z, hidden_units)
if node_attributes is not None:
self.node_attributes_lookup = nn.Embedding.from_pretrained(
node_attributes
)
self.node_attributes_lookup.weight.requires_grad = False
if edge_weights is not None:
self.edge_weights_lookup = nn.Embedding.from_pretrained(
edge_weights
)
self.edge_weights_lookup.weight.requires_grad = False
if node_embedding is not None:
self.node_embedding = nn.Embedding.from_pretrained(node_embedding)
self.node_embedding.weight.requires_grad = False
elif use_embedding:
self.node_embedding = nn.Embedding(num_nodes, hidden_units)
initial_dim = hidden_units
if self.use_attribute:
initial_dim += self.node_attributes_lookup.embedding_dim
if self.use_embedding:
initial_dim += self.node_embedding.embedding_dim
self.layers = nn.ModuleList()
if gcn_type == "gcn":
self.layers.append(
GraphConv(initial_dim, hidden_units, allow_zero_in_degree=True)
)
for _ in range(num_layers - 1):
self.layers.append(
GraphConv(
hidden_units, hidden_units, allow_zero_in_degree=True
)
)
self.layers.append(
GraphConv(hidden_units, 1, allow_zero_in_degree=True)
)
elif gcn_type == "sage":
self.layers.append(
SAGEConv(initial_dim, hidden_units, aggregator_type="gcn")
)
for _ in range(num_layers - 1):
self.layers.append(
SAGEConv(hidden_units, hidden_units, aggregator_type="gcn")
)
self.layers.append(SAGEConv(hidden_units, 1, aggregator_type="gcn"))
else:
raise ValueError("Gcn type error.")
self.pooling = SortPooling(k=k)
conv1d_channels = [16, 32]
total_latent_dim = hidden_units * num_layers + 1
conv1d_kws = [total_latent_dim, 5]
self.conv_1 = nn.Conv1d(
1, conv1d_channels[0], conv1d_kws[0], conv1d_kws[0]
)
self.maxpool1d = nn.MaxPool1d(2, 2)
self.conv_2 = nn.Conv1d(
conv1d_channels[0], conv1d_channels[1], conv1d_kws[1], 1
)
dense_dim = int((k - 2) / 2 + 1)
dense_dim = (dense_dim - conv1d_kws[1] + 1) * conv1d_channels[1]
self.linear_1 = nn.Linear(dense_dim, 128)
self.linear_2 = nn.Linear(128, 1)
def forward(self, g, z, node_id=None, edge_id=None):
"""
Args:
g(DGLGraph): the graph
z(Tensor): node labeling tensor, shape [N, 1]
node_id(Tensor, optional): node id tensor, shape [N, 1]
edge_id(Tensor, optional): edge id tensor, shape [E, 1]
Returns:
x(Tensor): output tensor
"""
z_emb = self.z_embedding(z)
if self.use_attribute:
x = self.node_attributes_lookup(node_id)
x = torch.cat([z_emb, x], 1)
else:
x = z_emb
if self.use_edge_weight:
edge_weight = self.edge_weights_lookup(edge_id)
else:
edge_weight = None
if self.use_embedding:
n_emb = self.node_embedding(node_id)
x = torch.cat([x, n_emb], 1)
xs = [x]
for layer in self.layers:
out = torch.tanh(layer(g, xs[-1], edge_weight=edge_weight))
xs += [out]
x = torch.cat(xs[1:], dim=-1)
# SortPooling
x = self.pooling(g, x)
x = x.unsqueeze(1)
x = F.relu(self.conv_1(x))
x = self.maxpool1d(x)
x = F.relu(self.conv_2(x))
x = x.view(x.size(0), -1)
x = F.relu(self.linear_1(x))
F.dropout(x, p=self.dropout, training=self.training)
x = self.linear_2(x)
return x