mindspore.ops.Tile
- class mindspore.ops.Tile[source]
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 \((x_1*y_1, x_2*y_2, ..., x_S*y_S)\).
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 \((1*y_1, ..., x_R*y_R, x_S*y_S)\).
- 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.]]]