stack

paddle. stack ( x: Sequence[Tensor], axis: int = 0, name: str | None = None ) → Tensor [source]

Stacks all the input tensors x along axis dimension. All tensors must be of the same shape and same dtype.

For example, given N tensors of shape [A, B], if axis == 0, the shape of stacked tensor is [N, A, B]; if axis == 1, the shape of stacked tensor is [A, N, B], etc.

It also supports the operation with zero-size tensors which contain 0 in their shape. See the examples below.

Case 1:

  Input:
    x[0].shape = [1, 2]
    x[0].data = [ [1.0 , 2.0 ] ]
    x[1].shape = [1, 2]
    x[1].data = [ [3.0 , 4.0 ] ]
    x[2].shape = [1, 2]
    x[2].data = [ [5.0 , 6.0 ] ]

  Attrs:
    axis = 0

  Output:
    Out.dims = [3, 1, 2]
    Out.data =[ [ [1.0, 2.0] ],
                [ [3.0, 4.0] ],
                [ [5.0, 6.0] ] ]


Case 2:

  Input:
    x[0].shape = [1, 2]
    x[0].data = [ [1.0 , 2.0 ] ]
    x[1].shape = [1, 2]
    x[1].data = [ [3.0 , 4.0 ] ]
    x[2].shape = [1, 2]
    x[2].data = [ [5.0 , 6.0 ] ]


  Attrs:
    axis = 1 or axis = -2  # If axis = -2, axis = axis+ndim(x[0])+1 = -2+2+1 = 1.

  Output:
    Out.shape = [1, 3, 2]
    Out.data =[ [ [1.0, 2.0]
                  [3.0, 4.0]
                  [5.0, 6.0] ] ]


Case 3:

    Input:
        x[0].shape = [0, 1, 2]
        x[0].data = []
        x[1].shape = [0, 1, 2]
        x[1].data = []

    Attrs:
        axis = 0

    Output:
        Out.shape = [2, 0, 1, 2]
        Out.data = []


Case 4:

    Input:
        x[0].shape = [0, 1, 2]
        x[0].data = []
        x[1].shape = [0, 1, 2]
        x[1].data = []

    Attrs:
        axis = 1

    Output:
        Out.shape = [0, 2, 1, 2]
        Out.data = []

The image below demonstrates the Case 1: three 2-dimensional tensors with shape [1, 2] are stacked in the dimension of axis=0 to form a 3-dimensional tensor with shape [3, 1, 2] .

Legend 1
Parameters

  • x (list[Tensor]|tuple[Tensor]) – Input x can be a list or tuple of tensors, the Tensors in x must be of the same shape and dtype. Supported data types: float32, float64, int32, int64.

  • axis (int, optional) – The axis along which all inputs are stacked. axis range is [-(R+1), R+1), where R is the number of dimensions of the first input tensor x[0]. If axis < 0, axis = axis+R+1. The default value of axis is 0.

  • name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor, The stacked tensor with same data type as input.

Examples

>>> import paddle

>>> x1 = paddle.to_tensor([[1.0, 2.0]])
>>> x2 = paddle.to_tensor([[3.0, 4.0]])
>>> x3 = paddle.to_tensor([[5.0, 6.0]])

>>> out = paddle.stack([x1, x2, x3], axis=0)
>>> print(out.shape)
[3, 1, 2]
>>> print(out)
Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 2.]],
 [[3., 4.]],
 [[5., 6.]]])

>>> out = paddle.stack([x1, x2, x3], axis=-2)
>>> print(out.shape)
[1, 3, 2]
>>> print(out)
Tensor(shape=[1, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 2.],
  [3., 4.],
  [5., 6.]]])

>>> # zero-size tensors
>>> x1 = paddle.ones([0, 1, 2])
>>> x2 = paddle.ones([0, 1, 2])

>>> out = paddle.stack([x1, x2], axis=0)
>>> print(out.shape)
[2, 0, 1, 2]
>>> print(out)
Tensor(shape=[2, 0, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[],
 []])

>>> out = paddle.stack([x1, x2], axis=1)
>>> print(out.shape)
[0, 2, 1, 2]
>>> print(out)
Tensor(shape=[0, 2, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[])