import torch
from cogdl.wrappers.model_wrapper import ModelWrapper
[docs]class PPRGoModelWrapper(ModelWrapper):
def __init__(self, model, optimizer_cfg):
super(PPRGoModelWrapper, self).__init__()
self.optimizer_cfg = optimizer_cfg
self.model = model
[docs] def train_step(self, batch):
x, targets, ppr_scores, y = batch
pred = self.model(x, targets, ppr_scores)
loss = self.default_loss_fn(pred, y)
return loss
[docs] def val_step(self, batch):
graph = batch
if isinstance(batch, list):
x, targets, ppr_scores, y = batch
pred = self.model(x, targets, ppr_scores)
else:
pred = self.model.predict(graph)
y = graph.y[graph.val_mask]
pred = pred[graph.val_mask]
loss = self.default_loss_fn(pred, y)
metric = self.evaluate(pred, y, metric="auto")
self.note("val_loss", loss.item())
self.note("val_metric", metric)
[docs] def test_step(self, batch):
graph = batch
if isinstance(batch, list):
x, targets, ppr_scores, y = batch
pred = self.model(x, targets, ppr_scores)
else:
pred = self.model.predict(graph)
test_mask = batch.test_mask
pred = pred[test_mask]
y = graph.y[test_mask]
loss = self.default_loss_fn(pred, y)
self.note("test_loss", loss.item())
self.note("test_metric", self.evaluate(pred, y))
[docs] def setup_optimizer(self):
cfg = self.optimizer_cfg
return torch.optim.Adam(self.model.parameters(), lr=cfg["lr"], weight_decay=cfg["weight_decay"])