Raising "list index out of range" error in torch_geometric/data/collate.py

🐛 Bug

When I used torch_geometric.data.Data as data structure to build an InMemoryDataset, the following error raised in the collate function.

File ".../torch_geometric/data/collate.py", line 184, in _collate
    and isinstance(elem[0], (Tensor, SparseTensor))):
IndexError: list index out of range

This error also ocurred in

File ".../torch_geometric/data/separate.py", line 92, in _separate
    and isinstance(value[0][0], (Tensor, SparseTensor))):
IndexError: list index out of range

To Reproduce

import torch
from torch_geometric.data import InMemoryDataset, download_url
from torch_geometric.data import Data

class MyOwnDataset(InMemoryDataset):
    def __init__(self, root, transform=None, pre_transform=None):
        super().__init__(root, transform, pre_transform)
        self.data, self.slices = torch.load(self.processed_paths[0])

    @property
    def raw_file_names(self):
        return ['some_file_1', 'some_file_2', ...]

    @property
    def processed_file_names(self):
        return ['data.pt']

    def download(self):
        # Download to `self.raw_dir`.
        download_url(url, self.raw_dir)

    def process(self):
        # Read data into huge `Data` list.
        data_list = []
        graph_input = Data(
            x=torch.from_numpy(x).float(),
            y=torch.from_numpy(y).float(),
            cluster=torch.from_numpy(cluster).short(),
            edge_index=torch.from_numpy(edge_index).long(),
            identifier=torch.from_numpy(identifier).float(),
            traj_len=torch.tensor([traj_lens[ind]]).int(),
            valid_len=torch.tensor([valid_lens[ind]]).int(),
            time_step_len=torch.tensor([num_valid_len_max]).int(),
            candidate_len_max=torch.tensor([num_candidate_max]).int(),
            candidate_mask=[],
            candidate=torch.from_numpy(raw_data['tar_candts'].values[0]).float(),
            candidate_gt=torch.from_numpy(raw_data['gt_candts'].values[0]).bool(),
            offset_gt=torch.from_numpy(raw_data['gt_tar_offset'].values[0]).float(),
            target_gt=torch.from_numpy(raw_data['gt_preds'].values[0][0][-1, :]).float(),
        )
        data_list.append(graph_input)
        data, slices = self.collate(data_list)
        torch.save((data, slices), self.processed_paths[0])

Expected behavior

No error raised.

Environment

• PyG version: torch_geometric.2.0.4
• PyTorch version: torch.1.10.1
• OS: Ubuntu16.04
• Python version: 3.9
• CUDA/cuDNN version: cuda11.3
• How you installed PyTorch and PyG: pip
• Any other relevant information: torch-scatter.2.0.9

Additional context

[alvarogunawan]

I think I’ve found what’s causing the issue with collate. I had an empty, non-tensor list as one of the data stores of the entries of my custom Dataset. I can see that in @McDifference’s example, they also have an empty list: candidate_mask=[]. Here’s a small example that generates the same error:

import torch
from torch_geometric.datasets import FakeHeteroDataset
from torch_geometric.loader import DataLoader
    
data = FakeHeteroDataset().data
data["v0"].nontensor = [] #empty, non-tensor list
dataset = [data, data]
loader = DataLoader(dataset, batch_size=2, shuffle=True)
print(data)
for b in loader:
    print(b)

This generates the following error:

HeteroData(
  v0={
    x=[1097, 67],
    y=[1097],
    nontensor=[0]
  },
  v1={ x=[1184, 55] },
  v2={ x=[958, 75] },
  (v2, e0, v1)={ edge_index=[2, 9537] },
  (v1, e0, v2)={ edge_index=[2, 11773] },
  (v2, e0, v0)={ edge_index=[2, 9539] },
  (v0, e0, v2)={ edge_index=[2, 10915] },
  (v0, e0, v1)={ edge_index=[2, 10913] },
  (v2, e0, v2)={ edge_index=[2, 9530] }
)
Traceback (most recent call last):
  File "dataset_test.py", line 10, in <module>
    for b in loader:
  File "/home/em11824/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
    data = self._next_data()
  File "/home/em11824/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 561, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/em11824/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
    return self.collate_fn(data)
  File "/home/em11824/.local/lib/python3.8/site-packages/torch_geometric/loader/dataloader.py", line 19, in __call__
    return Batch.from_data_list(batch, self.follow_batch,
  File "/home/em11824/.local/lib/python3.8/site-packages/torch_geometric/data/batch.py", line 68, in from_data_list
    batch, slice_dict, inc_dict = collate(
  File "/home/em11824/.local/lib/python3.8/site-packages/torch_geometric/data/collate.py", line 84, in collate
    value, slices, incs = _collate(attr, values, data_list, stores,
  File "/home/em11824/.local/lib/python3.8/site-packages/torch_geometric/data/collate.py", line 184, in _collate
    and isinstance(elem[0], (Tensor, SparseTensor))):
IndexError: list index out of range

I still need some empty lists in my dataset, so I was able to fix it by changing them from normal lists to empty tensors. I think the error is caused by the following in _collate:

https://github.com/pyg-team/pytorch_geometric/blob/57c88c0c7b86fb38715b72f5914bb208c7aa11e7/torch_geometric/data/collate.py#L183-L184

When one of the data stores is an empty list, it is a Sequence. However, it doesn’t have any elements, so elem[0] throws an index out of range error. The issue with _separate might be similar, but I didn’t encounter it myself.

[zmyzxb]

It’s seems that your code is based on a lower version of torch-geometric. I just solved the same problem by uninstalling pyg(from conda) and reinstall torch-geometric=1.7.2 with pip.