import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from cogdl.layers import SELayer
from .. import BaseModel, register_model
from .gcn import GraphConvolution
from cogdl.utils import add_remaining_self_loops, symmetric_normalization
[docs]@register_model("drgcn")
class DrGCN(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=16)
parser.add_argument("--num-layers", type=int, default=2)
parser.add_argument("--dropout", type=float, default=0.5)
# fmt: on
[docs] @classmethod
def build_model_from_args(cls, args):
return cls(
args.num_features,
args.num_classes,
args.hidden_size,
args.num_layers,
args.dropout,
)
def __init__(self, num_features, num_classes, hidden_size, num_layers, dropout):
super(DrGCN, self).__init__()
self.num_features = num_features
self.num_classes = num_classes
self.hidden_size = hidden_size
self.num_layers = num_layers
self.dropout = dropout
shapes = [num_features] + [hidden_size] * (num_layers - 1) + [num_classes]
self.convs = nn.ModuleList([GraphConvolution(shapes[layer], shapes[layer + 1]) for layer in range(num_layers)])
self.ses = nn.ModuleList(
[SELayer(shapes[layer], se_channels=int(np.sqrt(shapes[layer]))) for layer in range(num_layers)]
)
[docs] def forward(self, x, edge_index):
x = self.ses[0](x)
edge_index, edge_weight = add_remaining_self_loops(edge_index)
edge_weight = symmetric_normalization(x.shape[0], edge_index, edge_weight)
for se, conv in zip(self.ses[1:], self.convs[:-1]):
x = F.relu(conv(x, edge_index, edge_weight))
x = se(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.convs[-1](x, edge_index, edge_weight)
return x
[docs] def predict(self, data):
return self.forward(data.x, data.edge_index)