ogbn-products using gatconv example code: Error: raise AttributeError( AttributeError: 'GlobalStorage' object has no attribute 'n_id' during testing.

[arake001]

The code is below. During training it works perfectly but while testing I get the following error.

raise AttributeError( AttributeError: 'GlobalStorage' object has no attribute 'n_id'.

"Pytorch_geometric version 2.2.0"

The issue is in function inference

def inference(self, x_all):
   pbar = tqdm(total=x_all.size(0) * self.num_layers)
   pbar.set_description('Evaluating')

   # Compute representations of nodes layer by layer, using *all*
   # available edges. This leads to faster computation in contrast to
   # immediately computing the final representations of each batch.
   for i in range(self.num_layers):
       xs = []
       for batch in subgraph_loader:
           **x = x_all[batch.n_id].to(device)**
           edge_index = batch.edge_index.to(device)**

import os.path as osp

import torch
import torch.nn.functional as F
from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
from torch.nn import Linear as Lin
from tqdm import tqdm

from torch_geometric.loader import NeighborLoader
from torch_geometric.nn import GATConv

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
root = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'products')
dataset = PygNodePropPredDataset('ogbn-products', root)
split_idx = dataset.get_idx_split()
evaluator = Evaluator(name='ogbn-products')
data = dataset[0].to(device, 'x', 'y')

train_loader = NeighborLoader(
   data,
   input_nodes=split_idx['train'],
   num_neighbors=[10, 10, 5],
   batch_size=512,
   shuffle=True,
   num_workers=12,
   persistent_workers=True,
)
subgraph_loader = NeighborLoader(
   data,
   input_nodes=None,
   num_neighbors=[-1],
   batch_size=2048,
   num_workers=12,
   persistent_workers=True,
)


class GAT(torch.nn.Module):
   def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
                heads):
       super().__init__()

       self.num_layers = num_layers

       self.convs = torch.nn.ModuleList()
       self.convs.append(GATConv(dataset.num_features, hidden_channels,
                                 heads))
       for _ in range(num_layers - 2):
           self.convs.append(
               GATConv(heads * hidden_channels, hidden_channels, heads))
       self.convs.append(
           GATConv(heads * hidden_channels, out_channels, heads,
                   concat=False))

       self.skips = torch.nn.ModuleList()
       self.skips.append(Lin(dataset.num_features, hidden_channels * heads))
       for _ in range(num_layers - 2):
           self.skips.append(
               Lin(hidden_channels * heads, hidden_channels * heads))
       self.skips.append(Lin(hidden_channels * heads, out_channels))

   def reset_parameters(self):
       for conv in self.convs:
           conv.reset_parameters()
       for skip in self.skips:
           skip.reset_parameters()

   def forward(self, x, edge_index):
       for i, (conv, skip) in enumerate(zip(self.convs, self.skips)):
           x = conv(x, edge_index) + skip(x)
           if i != self.num_layers - 1:
               x = F.elu(x)
               x = F.dropout(x, p=0.5, training=self.training)
       return x

   def inference(self, x_all):
       pbar = tqdm(total=x_all.size(0) * self.num_layers)
       pbar.set_description('Evaluating')

       # Compute representations of nodes layer by layer, using *all*
       # available edges. This leads to faster computation in contrast to
       # immediately computing the final representations of each batch.
       for i in range(self.num_layers):
           xs = []
           for batch in subgraph_loader:
               **x = x_all[batch.n_id].to(device)**
               edge_index = batch.edge_index.to(device)
               x = self.convs[i](x, edge_index) + self.skips[i](x)
               x = x[:batch.batch_size]
               if i != self.num_layers - 1:
                   x = F.elu(x)
               xs.append(x.cpu())

               pbar.update(batch.batch_size)

           x_all = torch.cat(xs, dim=0)

       pbar.close()

       return x_all


model = GAT(dataset.num_features, 128, dataset.num_classes, num_layers=3,
           heads=4).to(device)


def train(epoch):
   model.train()

   pbar = tqdm(total=split_idx['train'].size(0))
   pbar.set_description(f'Epoch {epoch:02d}')

   total_loss = total_correct = 0
   for batch in train_loader:
       optimizer.zero_grad()
       out = model(batch.x, batch.edge_index.to(device))[:batch.batch_size]
       y = batch.y[:batch.batch_size].squeeze()
       loss = F.cross_entropy(out, y)
       loss.backward()
       optimizer.step()

       total_loss += float(loss)
       total_correct += int(out.argmax(dim=-1).eq(y).sum())
       pbar.update(batch.batch_size)

   pbar.close()

   loss = total_loss / len(train_loader)
   approx_acc = total_correct / split_idx['train'].size(0)

   return loss, approx_acc


@torch.no_grad()
def test():
   model.eval()

   out = model.inference(data.x)

   y_true = data.y.cpu()
   y_pred = out.argmax(dim=-1, keepdim=True)

   train_acc = evaluator.eval({
       'y_true': y_true[split_idx['train']],
       'y_pred': y_pred[split_idx['train']],
   })['acc']
   val_acc = evaluator.eval({
       'y_true': y_true[split_idx['valid']],
       'y_pred': y_pred[split_idx['valid']],
   })['acc']
   test_acc = evaluator.eval({
       'y_true': y_true[split_idx['test']],
       'y_pred': y_pred[split_idx['test']],
   })['acc']

   return train_acc, val_acc, test_acc


test_accs = []
for run in range(1, 11):
   print(f'\nRun {run:02d}:\n')

   model.reset_parameters()
   optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

   best_val_acc = final_test_acc = 0.0
   for epoch in range(1, 101):
       loss, acc = train(epoch)
       print(f'Epoch {epoch:02d}, Loss: {loss:.4f}, Approx. Train: {acc:.4f}')

       if epoch > 50 and epoch % 10 == 0:
           train_acc, val_acc, test_acc = test()
           print(f'Train: {train_acc:.4f}, Val: {val_acc:.4f}, '
                 f'Test: {test_acc:.4f}')

           if val_acc > best_val_acc:
               best_val_acc = val_acc
               final_test_acc = test_acc
   test_accs.append(final_test_acc)

test_acc = torch.tensor(test_accs)
print('============================')
print(f'Final Test: {test_acc.mean():.4f} ± {test_acc.std():.4f}')

[rusty1s]

You can do

data.n_id = torch.arange(data.num_nodes)

right after getting data from dataset. In later versions of PyG, this attribute is automatically created.