mindspore.ops.split

mindspore.ops.split(input_x, axis=0, output_num=1)[source]

Splits the input tensor into output_num of tensors along the given axis and output numbers.

The input_x tensor will be split into equally sized sub-tensors. This requires that input_x.shape(axis) is divisible by output_num.

Parameters

  • input_x (Tensor) – The shape of tensor is \((x_1, x_2, ..., x_R)\).

  • axis (int) – Index of the split position. Default: 0.

  • output_num (int) – The number of output tensors. Must be positive int. Default: 1.

Returns

tuple[Tensor], the shape of each output tensor is the same, which is \((y_1, y_2, ..., y_S)\). And the data type is the same with input_x.

Raises

  • TypeError – If axis or output_num is not an int.

  • ValueError – If axis is out of the range [-len(input_x.shape), len(input_x.shape)), or if the output_num is less than or equal to 0.

  • ValueError – If input_x.shape(axis) is not divisible by output_num.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]), mindspore.int32)
>>> print(x)
[[1 1 1 1]
 [2 2 2 2]]
>>> output = ops.split(x, 1, 2)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Int32, value=
[[1, 1],
 [2, 2]]), Tensor(shape=[2, 2], dtype=Int32, value=
[[1, 1],
 [2, 2]]))
>>> output = ops.split(x, 1, 4)
>>> print(output)
(Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]))