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.

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])