mindspore.ops.Tile

class mindspore.ops.Tile

Replicates an input tensor with given multiples times.

Refer to mindspore.ops.tile() for more details.

Inputs:

• input_x (Tensor) - 1-D or higher dimensional Tensor. Set the shape of input tensor as $(x_1,x_2,...,x_S)$ .

• multiples (tuple[int]) - The parameter that specifies the number of replications, the parameter type is tuple, and the data type is int, i.e., $(y_1,y_2,...,y_S)$. The length of multiples cannot be smaller than the length of the shape of input_x. Only constant value is allowed.

Outputs:

Tensor, has the same data type as the input_x. Suppose the length of multiples is d, the dimension of input_x is input_x.dim, and the shape of input_x is $(x_1,x_2,...,x_S)$.

• If input_x.dim = d, then the shape of their corresponding positions can be multiplied, and the shape of Outputs is $(x_1\ast y_1,x_2\ast y_2,...,x_S\ast y_S)$.

• If input_x.dim < d, fill in multiple 1 in the length of the shape of input_x until their lengths are consistent. Such as set the shape of input_x as $(1,...,x_1,x_2,...,x_S)$, then the shape of their corresponding positions can be multiplied, and the shape of Outputs is $(1\ast y_1,...,x_R\ast y_R,x_S\ast y_S)$.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> tile = ops.Tile()
>>> input_x = Tensor(np.array([[1, 2], [3, 4]]), mindspore.float32)
>>> multiples = (2, 3)
>>> output = tile(input_x, multiples)
>>> print(output)
[[1.  2.  1.  2.  1.  2.]
 [3.  4.  3.  4.  3.  4.]
 [1.  2.  1.  2.  1.  2.]
 [3.  4.  3.  4.  3.  4.]]
>>> multiples = (2, 3, 2)
>>> output = tile(input_x, multiples)
>>> print(output)
[[[1. 2. 1. 2.]
  [3. 4. 3. 4.]
  [1. 2. 1. 2.]
  [3. 4. 3. 4.]
  [1. 2. 1. 2.]
  [3. 4. 3. 4.]]
 [[1. 2. 1. 2.]
  [3. 4. 3. 4.]
  [1. 2. 1. 2.]
  [3. 4. 3. 4.]
  [1. 2. 1. 2.]
  [3. 4. 3. 4.]]]