oneflow.nansum

oneflow.nansum(input, dim, keepdim=False, *, dtype=None) → Tensor

Returns the sum of each row of the input tensor in the given dimension dim, treating Not a Numbers (NaNs) as zero. If dim is a list of dimensions, reduce over all of them.

If keepdim is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1. Otherwise, dim is squeezed (see oneflow.squeeze()), resulting in the output tensor having 1 (or len(dim)) fewer dimension(s).

The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nansum.html.

Parameters

  • input (oneflow.Tensor) – the Input Tensor

  • dim (int, optional) – the dimension to reduce. Default: None

  • keepdim (bool, optional) – whether the output tensor has dim retained or not. Default: False

  • dtype (oneflow.dtype, optional) – the desired data type of returned tensor. If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. Default: None.

Example:

>>> import oneflow as flow
>>> x = flow.tensor([1., 2., float("nan")])
>>> flow.nansum(x)
tensor(3., dtype=oneflow.float32)
>>> x = flow.tensor([[1., float("nan")], [float("nan"), 2]])
>>> flow.nansum(x, dim=1)
tensor([1., 2.], dtype=oneflow.float32)
>>> x = flow.tensor([float("nan") for i in range(3)])
>>> flow.nansum(x)
tensor(0., dtype=oneflow.float32)