def setup_class(self): self.dataset = build_dataset_from_name("cora") self.data = Data.from_pyg_data(self.dataset[0]) self.num_nodes = self.data.num_nodes self.num_edges = self.data.num_edges self.num_features = self.data.num_features print("Call Setup")
def fit(self, model, dataset): self.data = Data.from_pyg_data(dataset[0]) self.train_loader = NeighborSampler(data=self.data, mask=self.data.train_mask, sizes=self.sample_size, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=True) self.test_loader = NeighborSampler(data=self.data, mask=None, sizes=[-1], batch_size=self.batch_size, shuffle=False) self.model = model.to(self.device) self.model.set_data_device(self.device) self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay) best_model = self.train() self.model = best_model acc, loss = self._test_step() return dict(Acc=acc["test"], ValAcc=acc["val"])