I'm trying to get a graph neural network code to run on a cluster (where the code that I used, always used to work perfectly fine up to half a year ago). I have the following versions:
Traceback (most recent call last):
File "GNN_20210503_alltimes_trainsep.py", line 442, in
for train in train_dataloader:
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 363, in next
data = self._next_data()
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 403, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
return self.collate_fn(data)
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/loader/dataloader.py", line 20, in call
self.exclude_keys)
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/data/batch.py", line 75, in from_data_list
exclude_keys=exclude_keys,
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 109, in collate
out_store.batch = repeat_interleave(repeats, device=device)
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 205, in repeat_interleave
outs = [torch.full((n, ), i, device=device) for i, n in enumerate(repeats)]
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 205, in
outs = [torch.full((n, ), i, device=device) for i, n in enumerate(repeats)]
RuntimeError: Providing a bool or integral fill value without setting the optional dtype or out arguments is currently unsupported. In PyTorch 1.7, when dtype and out are not set a bool fill value will return a tensor of torch.bool dtype, and an integral fill value will return a tensor of torch.long dtype.
Downscaling the versions, as is suggested to others with this problem does unfortunately not work. Does anyone know how to fix this? Thank you in advance,
Hi all,
I'm trying to get a graph neural network code to run on a cluster (where the code that I used, always used to work perfectly fine up to half a year ago). I have the following versions:
python 3.6.9 torch 1.6.0 torch-geometric 2.0.3 torch-scatter 2.0.5 torch-sparse 0.6.8 torchvision 0.7.0
If I run the code, I get the following error:
Traceback (most recent call last): File "GNN_20210503_alltimes_trainsep.py", line 442, in
for train in train_dataloader:
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 363, in next
data = self._next_data()
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 403, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
return self.collate_fn(data)
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/loader/dataloader.py", line 20, in call
self.exclude_keys)
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/data/batch.py", line 75, in from_data_list
exclude_keys=exclude_keys,
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 109, in collate
out_store.batch = repeat_interleave(repeats, device=device)
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 205, in repeat_interleave
outs = [torch.full((n, ), i, device=device) for i, n in enumerate(repeats)]
File "/home/alkemade/.conda/envs/ML8/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 205, in
outs = [torch.full((n, ), i, device=device) for i, n in enumerate(repeats)]
RuntimeError: Providing a bool or integral fill value without setting the optional
dtypeoroutarguments is currently unsupported. In PyTorch 1.7, whendtypeandoutare not set a bool fill value will return a tensor of torch.bool dtype, and an integral fill value will return a tensor of torch.long dtype.Downscaling the versions, as is suggested to others with this problem does unfortunately not work. Does anyone know how to fix this? Thank you in advance,
Best Rinske