I have tried to run this code in colab, everything fine until i got an error when trying to run main function, here is the details :
Logging to /content/logs/TextGNN_twitter_asian_prejudice_small_0.8_0.1_0.1_2022-07-31T22-02-48.769172
Trying to load but no file /content/save/split/twitter_asian_prejudice_small_train_80_val_10_test_10_seed_3_window_size_10.klepto
Trying to load but no file /content/save/all/twitter_asian_prejudice_small_all_window_10.klepto
100%
1503/1503 [00:00<00:00, 15779.03it/s]
Saving to /content/save/all/twitter_asian_prejudice_small_all_window_10.klepto
Saving to /content/save/split/twitter_asian_prejudice_small_train_80_val_10_test_10_seed_3_window_size_10.klepto
(1183, 1183)
Number params: 237604
AttributeError Traceback (most recent call last)
[<ipython-input-22-263240bbee7e>](https://localhost:8080/#) in <module>()
----> 1 main()
9 frames
[<ipython-input-21-6463f33e79c1>](https://localhost:8080/#) in main()
10 if COMET_EXPERIMENT:
11 with COMET_EXPERIMENT.train():
---> 12 saved_model, model = train(train_data, val_data, saver)
13 else:
14 saved_model, model = train(train_data, val_data, saver)
[<ipython-input-18-4c4921548f33>](https://localhost:8080/#) in train(train_data, val_data, saver)
18 model.train()
19 model.zero_grad()
---> 20 loss, preds_train = model(pyg_graph, train_data)
21 loss.backward()
22 optimizer.step()
[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
[<ipython-input-14-a3723756fb22>](https://localhost:8080/#) in forward(self, pyg_graph, dataset)
31 for i, layer in enumerate(self.layers):
32 ins = acts[-1]
---> 33 outs = layer(ins, pyg_graph)
34 acts.append(outs)
35
[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
[<ipython-input-14-a3723756fb22>](https://localhost:8080/#) in forward(self, ins, pyg_graph)
101 x = self.conv(ins, pyg_graph.edge_index, edge_weight=pyg_graph.edge_attr)
102 else:
--> 103 x = self.conv(ins, pyg_graph.edge_index)
104 else:
105 x = self.conv(ins, pyg_graph.edge_index)
[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
[<ipython-input-14-a3723756fb22>](https://localhost:8080/#) in forward(self, x, edge_index, edge_weight)
254 if not self.cached or self.cached_result is None:
255 edge_index, norm = GCNConv.norm(edge_index, x.size(0), edge_weight,
--> 256 self.improved, x.dtype)
257 self.cached_result = edge_index, norm
258
[<ipython-input-14-a3723756fb22>](https://localhost:8080/#) in norm(edge_index, num_nodes, edge_weight, improved, dtype)
231
232 edge_index, edge_weight = remove_self_loops(edge_index, edge_weight)
--> 233 edge_index = add_self_loops(edge_index, num_nodes)
234 loop_weight = torch.full((num_nodes, ),
235 1 if not improved else 2,
[/usr/local/lib/python3.7/dist-packages/torch_geometric/utils/loop.py](https://localhost:8080/#) in add_self_loops(edge_index, edge_attr, fill_value, num_nodes)
123 if edge_attr is not None:
124 if fill_value is None:
--> 125 loop_attr = edge_attr.new_full((N, ) + edge_attr.size()[1:], 1.)
126
127 elif isinstance(fill_value, (int, float)):
AttributeError: 'int' object has no attribute 'new_full'