[Bug] AttributeError: 'PackedMolecule' object has no attribute 'node_position'

[bhadreshpsavani]

For property prediction when we replace model to SchNet in the tutorial and keeping other parameters as it is, we recieves below error.

Note: I used colab version of the tutorial.

AttributeError                            Traceback (most recent call last)
[<ipython-input-34-8206e318900d>](https://localhost:8080/#) in <module>()
      2 solver = core.Engine(task, train_set, valid_set, test_set, optimizer,
      3                      gpus=[0], batch_size=1024)
----> 4 solver.train(num_epoch=100)
      5 solver.evaluate("valid")

8 frames
[/usr/local/lib/python3.7/dist-packages/torchdrug/core/engine.py](https://localhost:8080/#) in train(self, num_epoch, batch_per_epoch)
    153                     batch = utils.cuda(batch, device=self.device)
    154 
--> 155                 loss, metric = model(batch)
    156                 if not loss.requires_grad:
    157                     raise RuntimeError("Loss doesn't require grad. Did you define any loss in the task?")

[/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 = [], []

[/usr/local/lib/python3.7/dist-packages/torchdrug/tasks/property_prediction.py](https://localhost:8080/#) in forward(self, batch)
     72         metric = {}
     73 
---> 74         pred = self.predict(batch, all_loss, metric)
     75 
     76         if all([t not in batch for t in self.task]):

[/usr/local/lib/python3.7/dist-packages/torchdrug/tasks/property_prediction.py](https://localhost:8080/#) in predict(self, batch, all_loss, metric)
    103     def predict(self, batch, all_loss=None, metric=None):
    104         graph = batch["graph"]
--> 105         output = self.model(graph, graph.node_feature.float(), all_loss=all_loss, metric=metric)
    106         pred = self.linear(output["graph_feature"])
    107         return pred

[/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 = [], []

[/usr/local/lib/python3.7/dist-packages/torchdrug/models/schnet.py](https://localhost:8080/#) in forward(self, graph, input, all_loss, metric)
     67 
     68         for layer in self.layers:
---> 69             hidden = layer(graph, layer_input)
     70             if self.short_cut and hidden.shape == layer_input.shape:
     71                 hidden = hidden + layer_input

[/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 = [], []

[/usr/local/lib/python3.7/dist-packages/torchdrug/layers/conv.py](https://localhost:8080/#) in forward(self, graph, input)
     89             update = checkpoint.checkpoint(self._message_and_aggregate, *graph.to_tensors(), input)
     90         else:
---> 91             update = self.message_and_aggregate(graph, input)
     92         output = self.combine(input, update)
     93         return output

[/usr/local/lib/python3.7/dist-packages/torchdrug/layers/conv.py](https://localhost:8080/#) in message_and_aggregate(self, graph, input)
    595     def message_and_aggregate(self, graph, input):
    596         node_in, node_out = graph.edge_list.t()[:2]
--> 597         position = graph.node_position
    598         rbf_weight = self.rbf_layer(self.rbf(position[node_in], position[node_out]))
    599         indices = torch.stack([node_out, node_in, torch.arange(graph.num_edge, device=graph.device)])

AttributeError: 'PackedMolecule' object has no attribute 'node_position'

[Oxer11]

Hi! This bug is because SchNet requires the 3D conformations of molecules to perform its convolution. But the datasets.Clintox dataset doesn't provide node positions for molecules. You can switch to datasets.QM9 and set node_position=True in the argument instead.