mindspore.mint.cat

mindspore.mint.cat(tensors, dim=0)

Connect input tensors along with the given dimension.

The input data is a tuple or a list of tensors. These tensors have the same rank $R$. Set the given dimension as $m$, and $0\le m<R$. Set the number of input tensors as $N$. For the $i$-th tensor $t_i$, it has the shape of $(x_1,x_2,...,x_{mi},...,x_R)$. $x_{mi}$ is the $m$-th dimension of the $t_i$. Then, the shape of the output tensor is

$$(x_1,x_2,...,\sum _{i=1}^N{x}_{mi},...,x_R)$$

Parameters

• tensors (Union[tuple, list]) – A tuple or a list of input tensors. Suppose there are two tensors in this tuple or list, namely t1 and t2. To perform concat in the dimension 0 direction, except for the $0$-th dimension, all other dimensions should be equal, that is, $t1.shape[1]=t2.shape[1],t1.shape[2]=t2.shape[2],...,t1.shape[R-1]=t2.shape[R-1]$, where $R$ represents the rank of tensor.

• dim (int, optional) – The specified dimension, whose value is in range $[-R,R)$. Default: 0 .

Returns

Tensor, the shape is $(x_1,x_2,...,\sum _{i=1}^N{x}_{mi},...,x_R)$. The data type is the same with tensors.

Raises

• TypeError – If dim is not an int.

• ValueError – If tensors have different dimension of tensor.

• ValueError – If dim not in range $[-R,R)$.

• ValueError – If tensor's shape in tensors except for dim are different.

• ValueError – If tensors is an empty tuple or list.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import mint
>>> input_x1 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
>>> input_x2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
>>> output = mint.cat((input_x1, input_x2))
>>> print(output)
[[0. 1.]
 [2. 1.]
 [0. 1.]
 [2. 1.]]
>>> output = mint.cat((input_x1, input_x2), 1)
>>> print(output)
[[0. 1. 0. 1.]
 [2. 1. 2. 1.]]