mindspore.numpy.dsplit

mindspore.numpy.dsplit(x, indices_or_sections)[source]

Splits a tensor into multiple sub-tensors along the 3rd axis (depth). It is equivalent to split with \(axis=2\) (default), the array is always split along the third axis regardless of the array dimension.

Parameters

x (Tensor) – A Tensor to be divided.
indices_or_sections (Union[int, tuple(int), list(int)]) – If integer, \(N\), the tensor will be divided into \(N\) equal tensors along axis. If tuple(int), list(int) or of sorted integers, the entries indicate where along axis the array is split. For example, \([2, 3]\) would, for \(axis=0\), result in three sub-tensors \(x[:2]\), \(x[2:3]\). If an index exceeds the dimension of the array along axis, an empty sub-array is returned correspondingly.

Returns

A list of sub-tensors.

Raises

TypeError – If argument indices_or_sections is not integer.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> input_x = np.arange(6).reshape((1, 2, 3)).astype('float32')
>>> output = np.dsplit(input_x, 3)
>>> print(output)
(Tensor(shape=[1, 2, 1], dtype=Float32,
value=[[[ 0.00000000e+00],
        [ 3.00000000e+00]]]),
Tensor(shape=[1, 2, 1], dtype=Float32,
value=[[[ 1.00000000e+00],
        [ 4.00000000e+00]]]),
Tensor(shape=[1, 2, 1], dtype=Float32,
value=[[[ 2.00000000e+00],
        [ 5.00000000e+00]]]))