oneflow.median¶

oneflow.median(input) → Tensor ¶

Returns the median of the values in input. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.median.html#torch.median

Note

The median is not unique for input tensors with an even number of elements. In this case the lower of the two medians is returned.

Parameters: input (Tensor) – the input tensor.

For example:

>>> import oneflow as flow
>>> x = flow.tensor((1, 2, -1), dtype=flow.float32)
>>> flow.median(x)
tensor(1., dtype=oneflow.float32)

oneflow.median(input, dim=- 1, keepdim=False, *, out=None)

Returns a tuple (values, indices) where values contains the median of each row of input in the dimension dim, and indices contains the index of the median values found in the dimension dim.

By default, dim is the last dimension of the input tensor.

If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see flow.squeeze()), resulting in the outputs tensor having 1 fewer dimension than input.

Note

The median is not unique for input tensors with an even number of elements in the dimension dim. In this case the lower of the two medians is returned.

Parameters

input (Tensor) – the input tensor.
dim (int) – the dimension to reduce.
keepdim (bool) – whether the output tensor has dim retained or not.

For example:

>>> import oneflow as flow
>>> a = flow.tensor([[ 0.2505, -0.3982, -0.9948,  0.3518, -1.3131],
...    [ 0.3180, -0.6993,  1.0436,  0.0438,  0.2270],
...    [-0.2751,  0.7303,  0.2192,  0.3321,  0.2488],
...    [ 1.0778, -1.9510,  0.7048,  0.4742, -0.7125]])
>>> result=flow.median(a, 1)
>>> result.values
tensor([-0.3982,  0.2270,  0.2488,  0.4742], dtype=oneflow.float32)
>>> result.indices
tensor([1, 4, 4, 3], dtype=oneflow.int64)