import numpy as np
import torch.nn.functional as F
from cogdl.layers import SELayer
from .. import BaseModel, register_model
from .gat import GATLayer
[docs]@register_model("drgat")
class DrGAT(BaseModel):
[docs] @staticmethod
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("--nhead", type=int, default=8)
parser.add_argument("--dropout", type=float, default=0.6)
# fmt: on
[docs] @classmethod
def build_model_from_args(cls, args):
return cls(
args.num_features,
args.num_classes,
args.hidden_size,
args.nhead,
args.dropout,
)
def __init__(self, num_features, num_classes, hidden_size, num_heads, dropout):
super(DrGAT, 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 = GATLayer(num_features, hidden_size, nhead=num_heads, attn_drop=dropout)
self.conv2 = GATLayer(hidden_size * num_heads, num_classes, nhead=1, attn_drop=dropout)
self.se1 = SELayer(num_features, se_channels=int(np.sqrt(num_features)))
self.se2 = SELayer(hidden_size * num_heads, se_channels=int(np.sqrt(hidden_size * num_heads)))
[docs] def forward(self, graph):
x = graph.x
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.se1(x)
x = F.elu(self.conv1(graph, x))
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.se2(x)
x = F.elu(self.conv2(graph, x))
return x