import torch
from .. import ModelWrapper
[docs]class GraphClassificationModelWrapper(ModelWrapper):
def __init__(self, model, optimizer_cfg):
super(GraphClassificationModelWrapper, self).__init__()
self.model = model
self.optimizer_cfg = optimizer_cfg
[docs] def train_step(self, batch):
pred = self.model(batch)
y = batch.y
loss = self.default_loss_fn(pred, y)
return loss
[docs] def val_step(self, batch):
pred = self.model(batch)
y = batch.y
val_loss = self.default_loss_fn(pred, y)
metric = self.evaluate(pred, y, metric="auto")
self.note("val_loss", val_loss)
self.note("val_metric", metric)
[docs] def test_step(self, batch):
pred = self.model(batch)
y = batch.y
test_loss = self.default_loss_fn(pred, y)
metric = self.evaluate(pred, y, metric="auto")
self.note("test_loss", test_loss)
self.note("test_metric", metric)
[docs] def setup_optimizer(self):
cfg = self.optimizer_cfg
return torch.optim.Adam(self.model.parameters(), lr=cfg["lr"], weight_decay=cfg["weight_decay"])