import torch
from cogdl.wrappers.model_wrapper import ModelWrapper
[docs]class HeterogeneousGNNModelWrapper(ModelWrapper):
def __init__(self, model, optimizer_cfg):
super(HeterogeneousGNNModelWrapper, self).__init__()
self.optimizer_cfg = optimizer_cfg
self.model = model
[docs] def train_step(self, batch):
graph = batch.data
pred = self.model(graph)
train_mask = graph.train_node
loss = self.default_loss_fn(pred[train_mask], graph.y[train_mask])
return loss
[docs] def val_step(self, batch):
graph = batch.data
pred = self.model(graph)
val_mask = graph.valid_node
loss = self.default_loss_fn(pred[val_mask], graph.y[val_mask])
metric = self.evaluate(pred[val_mask], graph.y[val_mask], metric="auto")
self.note("val_loss", loss.item())
self.note("val_metric", metric)
[docs] def test_step(self, batch):
graph = batch.data
pred = self.model(graph)
test_mask = graph.test_node
loss = self.default_loss_fn(pred[test_mask], graph.y[test_mask])
metric = self.evaluate(pred[test_mask], graph.y[test_mask], metric="auto")
self.note("test_loss", loss.item())
self.note("test_metric", metric)
[docs] def setup_optimizer(self):
cfg = self.optimizer_cfg
if hasattr(self.model, "get_optimizer"):
model_spec_optim = self.model.get_optimizer(cfg)
if model_spec_optim is not None:
return model_spec_optim
return torch.optim.Adam(self.model.parameters(), lr=cfg["lr"], weight_decay=cfg["weight_decay"])