RuntimeError: tensors used as indices must be long or byte tensors

[Supermaxman]

I am trying to run the examples.simple.main.py and I encounter the following exception:

RuntimeError: tensors used as indices must be long or byte tensors

I have the following packages installed with pip3 in python3.6:

neat-python==0.92
numpy==1.15.2+mkl
gym==0.10.5
click==6.7
torch==0.4.0
torchvision==0.2.1

This error occurs within pytorch_neat.recurrent_net in the dense_from_coo function:

def dense_from_coo(shape, conns, dtype=torch.float64):
    mat = torch.zeros(shape, dtype=dtype)
    idxs, weights = conns
    if len(idxs) == 0:
        return mat
    rows, cols = np.array(idxs).transpose()
    mat[torch.tensor(rows), torch.tensor(cols)] = torch.tensor(
        weights, dtype=dtype)
    return mat

The problem is that np.array is assuming int32 for the indexes, but torch wants int64.
Simple solution:

def dense_from_coo(shape, conns, dtype=torch.float64):
    mat = torch.zeros(shape, dtype=dtype)
    idxs, weights = conns
    if len(idxs) == 0:
        return mat
    rows, cols = np.array(idxs, dtype=np.int64).transpose()
    mat[torch.tensor(rows), torch.tensor(cols)] = torch.tensor(
        weights, dtype=dtype)
    return mat

The difference may be from the differing numpy versions, but I think this change makes sense regardless for torch tensor indexing.

[ai-nick]

Hey supermax, I had the same issue and I solved it by instead changing mat[torch.tensor(rows), torch.tensor(cols)] to mat[torch.LongTensor(rows), torch.LongTensor(cols)] idk if this would alleviate the differences in numpy versions or not