mindspore.ops.split

mindspore.ops.split(tensor, split_size_or_sections, axis=0)[source]

Splits the Tensor into chunks along the given axis.

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[axis] 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 axis.

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

Returns

A tuple of sub-tensors.

Raises

  • TypeError – If argument tensor is not Tensor.

  • TypeError – If argument axis is not Tensor.

  • ValueError – If argument axis is out of range of \([-tensor.ndim, tensor.ndim)\) .

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

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

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> input_x = np.arange(9).astype("float32")
>>> output = ops.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]))