mindspore.mint.cummax

mindspore.mint.cummax(input, dim)

Returns a tuple (values, indices) where values is the cumulative maximum value of input Tensor input along the dimension dim, and indices is the index location of each maximum value.

\[\begin{split}\begin{array}{ll} \\ y_{i} = \max(x_{1}, x_{2}, ... , x_{i}) \end{array}\end{split}\]

Parameters

• input (Tensor) – The input Tensor. Rank of input must be greater than 0.

• dim (int) – The dimension to do the operation over. The value of dim must be in the range [-input.ndim, input.ndim - 1].

Returns

tuple [Tensor], tuple of 2 Tensors, containing the cumulative maximum of elements and the index. The shape of each output tensor is the same as that of input input.

Raises

• TypeError – If input is not a Tensor.

• TypeError – If dim is not an int.

• ValueError – If dim is out the range of [-input.ndim, input.ndim - 1].

Note

O2 mode is not supported in Ascend.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import mint
>>> x = Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float32))
>>> output = mint.cummax(x, dim=0)
>>> print(output[0])
[[ 3.  4.  6. 10.]
 [ 3.  6.  7. 10.]
 [ 4.  6.  8. 10.]
 [ 4.  6.  8. 10.]]
>>> print(output[1])
[[0 0 0 0]
 [0 1 1 0]
 [2 1 2 0]
 [2 1 2 0]]