torch.tensor(ndarray) works fine for me. However, the labels are a binary mask so I’m storing them as an ndarray of numpy int8. The following code:
label_dict[key] = torch.Tensor(label)
fails with:
RuntimeError: tried to construct a tensor from a nested float sequence, but found an item of type numpy.int8 at index (0, 0, 0)
If label is converted  with .astype(np.uint8) ahead of time, I get:
RuntimeError: tried to construct a tensor from a nested int sequence, but found an item of type numpy.uint8 at index (0, 0, 0)
I can obviously preallocate an appropriately sized CharTensor and then copy myself, but it seems like that shouldn’t be necessary.