mindspore.Tensor.repeat_interleave

Tensor.repeat_interleave(repeats, dim=None, *, output_size=None) → Tensor[source]

Repeat elements of a tensor along a dim, like mindspore.numpy.repeat().

Warning

Non-backward-compatible change after version 2.9.0: overload repeat_interleave(repeats, dim=None) will be removed, and only repeat_interleave(repeats, dim=None, *, output_size=None) will be supported.

Note

The self tensor to repeat values for. Must be of type: float16, float32, int8, uint8, int16, int32, or int64.

Parameters:

repeats (Union[int, tuple, list, Tensor]) – The number of times to repeat, must be positive.
dim (int, optional) – The dim along which to repeat, Default: None. If dim is None, the self Tensor will be flattened and the output will also be flattened.

Keyword Arguments:

output_size (int, optional) – Total output size for the given axis (e.g. sum of repeats), Default: None.

Returns:

One tensor with values repeated along the specified dim. If self has shape \((s1, s2, ..., sn)\) and dim is i, the output will have shape \((s1, s2, ..., si * repeats, ..., sn)\). The output type will be the same as the type of self.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> input1 = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
>>> output1 = input1.repeat_interleave(repeats=2, dim=0, output_size=None)
>>> input2 = Tensor(np.array([[1, 2], [3, 4]]), mindspore.int32)
>>> output2 = input2.repeat_interleave(Tensor(np.array([1, 2])), dim=0, output_size=None)
>>> print(output1)
>>> print(output2)
[[0 1 2]
 [0 1 2]
 [3 4 5]
 [3 4 5]]
[[1 2]
 [3 4]
 [3 4]]

Tensor.repeat_interleave(repeats, dim=None) → Tensor[source]