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

\[(x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)\]

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) – The specified dimension, whose value is in range \([-R, R)\). Default: 0 .

Returns

Tensor, the shape is \((x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)\). 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.]]