import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn.conv import GCNConv
from cogdl.layers import SELayer
from .. import BaseModel, register_model
[docs]@register_model("drgcn")
class DrGCN(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=16)
parser.add_argument("--num-layers", type=int, default=2)
parser.add_argument("--dropout", type=float, default=0.5)
# fmt: on
@classmethod
[docs] 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(
[
GCNConv(shapes[layer], shapes[layer + 1], cached=True)
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)
for se, conv in zip(self.ses[1:], self.convs[:-1]):
x = F.relu(conv(x, edge_index))
x = se(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.convs[-1](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)