oneflow.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, 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 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.

For example:

>>> 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[0] = -1
>>> a
array([1, 2, 3])