mindspore.Tensor.repeat_interleave
- Tensor.repeat_interleave(repeats, dim=None, *, output_size=None) Tensor
Repeat elements of a tensor along a dim, like 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 \((s1, s2, ..., sn)\) 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, ops >>> 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 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 \((s1, s2, ..., sn)\) 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
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> 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]]