as_tensor(data, dtype=None, device=None) → Tensor¶
Converts data into a tensor, sharing data and preserving autograd history if possible.
If data is already a tensor with the requeseted 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.tensor()never shares its data and creates a new “leaf tensor” (see Autograd mechanics).
data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy
ndarray, scalar, and other types.
torch.dtype, optional) – the desired data type of returned tensor. Default: if
None, infers data type from
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 CPU.
>>> a = numpy.array([1, 2, 3]) >>> t = torch.as_tensor(a) >>> t tensor([ 1, 2, 3]) >>> t = -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 = -1 >>> a array([1, 2, 3])