Source code for cogdl.models.nn.drgat

import numpy as np
import torch.nn.functional as F

from cogdl.layers import SELayer, GATLayer

from .. import BaseModel

[docs]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, x = self.se1(x) x = F.elu(self.conv1(graph, x)) x = F.dropout(x, p=self.dropout, x = self.se2(x) x = F.elu(self.conv2(graph, x)) return x