torch.as_tensor

torch.as_tensor(data: Any, dtype: Optional[dtype] = None, device: Optional[DeviceLikeType]) → Tensor

Converts data into a tensor, sharing data and preserving autograd history if possible.

If data is already a tensor with the requested dtype and device then data itself is returned, but if data is a tensor with a different dtype or device then it’s copied as if using data.to(dtype=dtype, device=device).

If data is a NumPy array (an ndarray) with the same dtype and device then a tensor is constructed using torch.from_numpy().

If data is a CuPy array, the returned tensor will be located on the same device as the CuPy array unless specifically overwritten by device or a default device. The device of the CuPy array is inferred from the pointer of the array using cudaPointerGetAttributes unless device is provided with an explicit device index.

See also

torch.tensor() never shares its data and creates a new “leaf tensor” (see Autograd mechanics).

Parameters

• data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, infers data type from data.

• device (torch.device, optional) – the device of the constructed tensor. If None and data is a tensor then the device of data is used. If None and data is not a tensor then the result tensor is constructed on the current device.

Example:

>>> a = numpy.array([1, 2, 3])
>>> t = torch.as_tensor(a)
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([-1,  2,  3])

>>> a = numpy.array([1, 2, 3])
>>> t = torch.as_tensor(a, device=torch.device('cuda'))
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([1,  2,  3])