torch / torch
torch.as_tensor¶
-
torch.
as_tensor
(data, dtype=None, device=None) → Tensor¶ Convert the data into a torch.Tensor. If the data is already a Tensor with the same dtype and device, no copy will be performed, otherwise a new Tensor will be returned with computational graph retained if data Tensor has
requires_grad=True
. Similarly, if the data is anndarray
of the corresponding dtype and the device is the cpu, no copy will be performed.- 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: ifNone
, infers data type fromdata
.device (
torch.device
, optional) – the desired device of returned tensor. Default: ifNone
, uses the current device for the default tensor type (seetorch.set_default_tensor_type()
).device
will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
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])
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