import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from cogdl.layers import SELayer, GCNLayer
from .. import BaseModel
[docs]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)
parser.add_argument("--norm", type=str, default=None)
parser.add_argument("--activation", type=str, default="relu")
# 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,
args.norm,
args.activation,
)
def __init__(self, num_features, num_classes, hidden_size, num_layers, dropout, norm=None, activation="relu"):
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(
[
GCNLayer(shapes[layer], shapes[layer + 1], activation=activation, norm=norm)
for layer in range(num_layers - 1)
]
)
self.convs.append(GCNLayer(shapes[-2], shapes[-1]))
self.ses = nn.ModuleList(
[SELayer(shapes[layer], se_channels=int(np.sqrt(shapes[layer]))) for layer in range(num_layers)]
)
[docs] def forward(self, graph):
graph.sym_norm()
x = graph.x
x = self.ses[0](x)
for se, conv in zip(self.ses[1:], self.convs[:-1]):
x = conv(graph, x)
x = se(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.convs[-1](graph, x)
return x
[docs] def predict(self, graph):
return self.forward(graph)