mindspore.ops.transpose
- mindspore.ops.transpose(input, input_perm)[source]
Permutes the dimensions of the input tensor according to input permutation.
For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector please refer to
mindspore.ops.expand_dims()
. For a 2-D array, this is a standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape is \((i[0], i[1], ... i[n-2], i[n-1])\), then a.transpose().shape is \((i[n-1], i[n-2], ... i[1], i[0])\).Note
On GPU and CPU, if the value of input_perm is negative, its actual value is input_perm[i] + rank(input). Negative value of input_perm is not supported on Ascend.
- Parameters
input (Tensor) – The shape of tensor is \((x_1, x_2, ..., x_R)\).
input_perm (tuple[int]) – The permutation to be converted. The elements in input_perm are composed of the indexes of each dimension of input. The length of input_perm and the shape of input must be the same. Only constant value is allowed. Must be in the range [-rank(input), rank(input)).
- Returns
Tensor, the type of output tensor is the same as input and the shape of output tensor is decided by the shape of input and the value of input_perm.
- Raises
TypeError – If input_perm is not a tuple.
ValueError – If length of shape of input is not equal to length of shape of input_perm.
ValueError – If the same element exists in input_perm.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> input = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32) >>> input_perm = (0, 2, 1) >>> output = ops.transpose(input, input_perm) >>> print(output) [[[ 1. 4.] [ 2. 5.] [ 3. 6.]] [[ 7. 10.] [ 8. 11.] [ 9. 12.]]]