mindspore.Tensor.repeat_interleave
- Tensor.repeat_interleave(repeats, dim=None, *, output_size=None) Tensor
Repeat elements of a tensor along a dim, like
mindspore.numpy.repeat()
.Warning
Only support on Atlas A2 training series.
Note
The self tensor to repeat values for. Must be of type: float16, float32, int8, uint8, int16, int32, or int64.
- Parameters
- 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
and dim is i, the output will have shape . 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
Repeat elements of a tensor along an dim, like
mindspore.numpy.repeat()
.Note
The tensor to repeat values for. Must be of type: float16, float32, int8, uint8, int16, int32, or int64.
- Parameters
- Returns
One tensor with values repeated along the specified dim. If self has shape
and dim is i, the output will have shape . The output type will be the same as the type of self.
- Supported Platforms:
Ascend
GPU
CPU
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) >>> input2 = Tensor(np.array([[1, 2], [3, 4]]), mindspore.int32) >>> output2 = input2.repeat_interleave(Tensor(np.array([1, 2])), dim=0) >>> print(output1) >>> print(output2) [[0 1 2] [0 1 2] [3 4 5] [3 4 5]] [[1 2] [3 4] [3 4]]