import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GraphUNet
from torch_geometric.utils import dropout_adj
from .. import BaseModel, register_model
from cogdl.utils import add_remaining_self_loops
[docs]@register_model("unet")
class UNet(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=32)
parser.add_argument("--num-layers", type=int, default=2)
parser.add_argument("--dropout", type=float, default=0.92)
# 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,
args.num_nodes
)
def __init__(self, num_features, num_classes, hidden_size, num_layers, dropout, num_nodes):
super(UNet, 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
self.num_nodes = 0
self.unet = GraphUNet(
self.num_features,
self.hidden_size,
self.num_classes,
depth=3,
pool_ratios=[2000 / num_nodes, 0.5],
act=F.elu
)
[docs] def forward(self, x, edge_index):
edge_index, _ = dropout_adj(edge_index, p=0.2,
force_undirected=True,
num_nodes=x.shape[0],
training=self.training)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.unet(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)