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 oneflow.from_numpy.
The interface is consistent with PyTorch.
data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy
ndarray, scalar, and other types.
dtype (oneflow.dtype, optional) – the desired data type of returned tensor. Default: if
None, infers data type from data.
device (oneflow.device, optional) – the device of the constructed tensor. If
Noneand 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.
>>> import oneflow as flow >>> import numpy as np >>> a = np.array([1, 2, 3]) >>> t = flow.as_tensor(a, device=flow.device('cuda')) >>> t tensor([1, 2, 3], device='cuda:0', dtype=oneflow.int64) >>> t = -1 >>> a array([1, 2, 3])