RuntimeError: Could not infer dtype of generator

I build a training data for a model using PyTorch:

def shufflerow(tensor1, tensor2, axis):
    row_perm = torch.rand(tensor1.shape[:axis+1]).argsort(axis)  # get permutation indices
    for _ in range(tensor1.ndim-axis-1): row_perm.unsqueeze_(-1)
    row_perm = row_perm.repeat(*[1 for _ in range(axis+1)], *(tensor1.shape[axis+1:]))  # reformat this for the gather operation
    return tensor1.gather(axis, row_perm),tensor2.gather(axis, row_perm)
class Dataset:
    def __init__(self, observation, next_observation):
        self.data =(observation, next_observation)
        indices = torch.randperm(observation.shape[0])
        self.train_samples = (observation[indices ,:], next_observation[indices ,:])
        self.test_samples = shufflerow(observation, next_observation, 0)

I also have this function which examines whether the data converted to torch.tensor and set the device:

def to_tensor(x, device):
    if torch.is_tensor(x):
        return x
    elif isinstance(x, np.ndarray):
        return torch.from_numpy(x).to(device=device, dtype=torch.float32)
    elif isinstance(x, list):
        if all(isinstance(item, np.ndarray) for item in x):
           return [torch.from_numpy(item).to(device=device, dtype=torch.float32) for item in x]
    elif isinstance(x, tuple):
        return (torch.from_numpy(item).to(device=device, dtype=torch.float32) for item in x)
    else:
        print(f"X:{x} and X's type{type(x)}") 
        return torch.tensor(x).to(device=device, dtype=torch.float32)

But passing the input data that basically looks like this through the Dataset class:

  data=Dataset(s1,s2)
  print(data.train_samples)

(tensor([[-0.3121, -0.9500,  1.4518],
        [-0.9903, -0.1391, -4.4141],
        [-0.9645, -0.2642,  5.0233],
        [-0.6413,  0.7673, -4.5495],
        [-0.3073,  0.9516, -1.0128],
        [-0.5495,  0.8355,  3.4044],
        [-0.5710, -0.8209, -3.2716],
        [-0.9388,  0.3445,  3.9225],
        [-0.8402, -0.5423, -4.0820]]), tensor([[-0.2723, -0.9622,  0.8342],
        [-0.9958,  0.0912, -4.6186],
        [-0.8747, -0.4847,  4.7741],
        [-0.5495,  0.8355,  3.4044],
        [-0.7146,  0.6996,  4.2841],
        [-0.7128, -0.7014, -3.7148],
        [-0.9915,  0.1303,  4.4200],
        [-0.9358, -0.3526, -4.2585]]))

I get this error message:

-> 1725         self._target_samples = to_tensor(true_samples)
   1726         self._steps = []


/content/data_gen.py in to_tensor(x)
   1368     else:
   1369         print(f"X:{x} and X's type{type(x)}")
-> 1370         return torch.tensor(x).to(device=device, dtype=torch.float32)
   
X:<generator object to_tensor.<locals>.<genexpr> at 0x7f380235d6d0> and X's type<class 'generator'>
RuntimeError: Could not infer dtype of generator

Any suggestion, why I am getting this error?

The expression (torch.from_numpy(item).to(device=device, dtype=torch.float32) for item in x) isn't creating a tuple, it's a generator expression. Since it's in a case where you test for tuples, I suspect you wanted a tuple instead of a generator. Try:

elif isinstance(x, tuple):
    return tuple(torch.from_numpy(item).to(device=device, dtype=torch.float32) for item in x)