mindspore.mint.split

mindspore.mint.split(tensor, split_size_or_sections, dim=0)[source]

Splits the Tensor into chunks along the given dim.

Parameters

  • tensor (Tensor) – A Tensor to be divided.

  • split_size_or_sections (Union[int, tuple(int), list(int)]) – If split_size_or_sections is an int type, tensor will be split into equally sized chunks, each chunk with size split_size_or_sections. Last chunk will be smaller than split_size_or_sections if tensor.shape[dim] is not divisible by split_size_or_sections. If split_size_or_sections is a list type, then tensor will be split into len(split_size_or_sections) chunks with sizes split_size_or_sections along the given dim.

  • dim (int) – The dim along which to split. Default: 0 .

Returns

A tuple of sub-tensors.

Raises

  • TypeError – If argument tensor is not Tensor.

  • TypeError – If argument dim is not int.

  • ValueError – If argument dim is out of range of :[-tensor.ndim, tensor.ndim).

  • TypeError – If each element in split_size_or_sections is not integer.

  • TypeError – If argument split_size_or_sections is not int, tuple(int) or list(int).

  • ValueError – The sum of split_size_or_sections is not equal to x.shape[dim].

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import mint, Tensor
>>> input_x = np.arange(9).astype("float32")
>>> output = mint.split(Tensor(input_x), 3)
>>> print(output)
(Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00]),
 Tensor(shape=[3], dtype=Float32, value= [ 3.00000000e+00,  4.00000000e+00,  5.00000000e+00]),
 Tensor(shape=[3], dtype=Float32, value= [ 6.00000000e+00,  7.00000000e+00,  8.00000000e+00]))