import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn.conv import GATConv
from .. import BaseModel, register_model
[docs]@register_model("pyg_gat")
class GAT(BaseModel):
@staticmethod
[docs] def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument("--num-features", type=int)
parser.add_argument("--num-classes", type=int)
parser.add_argument("--hidden-size", type=int, default=8)
parser.add_argument("--num-heads", type=int, default=8)
parser.add_argument("--dropout", type=float, default=0.6)
parser.add_argument("--lr", type=float, default=0.005)
# fmt: on
@classmethod
[docs] def build_model_from_args(cls, args):
return cls(
args.num_features,
args.num_classes,
args.hidden_size,
args.num_heads,
args.dropout,
)
def __init__(self, num_features, num_classes, hidden_size, num_heads, dropout):
super(GAT, self).__init__()
self.num_features = num_features
self.num_classes = num_classes
self.hidden_size = hidden_size
self.num_heads = num_heads
self.dropout = dropout
self.conv1 = GATConv(
num_features, hidden_size, heads=num_heads, dropout=dropout
)
self.conv2 = GATConv(hidden_size * num_heads, num_classes, dropout=dropout)
[docs] def forward(self, x, edge_index):
x = F.dropout(x, p=self.dropout, training=self.training)
x = F.elu(self.conv1(x, edge_index))
x = F.dropout(x, p=self.dropout, training=self.training)
x = F.elu(self.conv2(x, edge_index))
return F.log_softmax(x, dim=1)
[docs] def loss(self, data):
return F.nll_loss(
self.forward(data.x, data.edge_index)[data.train_mask],
data.y[data.train_mask],
)
[docs] def predict(self, data):
return self.forward(data.x, data.edge_index)