def tensor(data: Union[list, np.ndarray] = None,
*,
dtype: str = None,
device: mgb.CompNode = None,
requires_grad: bool = None):
r"""A helper function to create a :class:`~.Tensor` using existing data.
:param data: an existing data array, must be Python list, NumPy array or None.
:param dtype: target Tensor data type, one of ``("uint8", "int8", "int16", "int32", "float32", "float16")``.
:param device: target device for Tensor storing.
:param requires_grad: whether its gradiant will be calculated during :meth:`~.Optimizer.backward`
"""
supported_dtypes = ("uint8", "int8", "int16", "int32", "float32",
"float16")
if isinstance(data, Tensor):
raise NotImplementedError
if dtype is not None and np.dtype(dtype).name not in supported_dtypes:
raise TypeError("unsupported dtype {}".format(dtype))
if data is not None:
if not isinstance(data, np.ndarray):
data = np.array(data, dtype=dtype)
# In order to accept tensor([1]),
# Automaticlly convert to 32-bit number instead of numpy's default 64-bit when input data is not nparray.
dtype = mgb.to_mgb_supported_dtype(data.dtype)
if dtype is None:
if data.dtype.name not in supported_dtypes:
raise TypeError("unsupported dtype {}".format(data.dtype))
device, _ = _use_default_if_none(device, None)
shared_nd = mgb.make_shared(device, value=data, dtype=dtype)
return Tensor(shared_nd, requires_grad=requires_grad)
def __setstate__(self, state):
data = state.pop("data")
device = state.pop("device")
dtype = state.pop("dtype")
metadata = state.pop("metadata", {})
requires_grad = metadata.pop("requires_grad", None)
snd = mgb.make_shared(device, value=data, dtype=dtype)
self._reset(snd, requires_grad=requires_grad)
def set_dtype(self, dtype: str = None):
r"""Set the data type of the tensor.
"""
if self.__val is not None:
self.__val = mgb.make_shared(self.device,
value=self.astype(dtype).numpy())
elif self.__sym is not None:
self.__sym = self.__sym.astype(dtype)
