gather_nd

paddle. gather_nd ( x, index, name=None ) [源代码]

该OP是 gather 的高维推广,并且支持多轴同时索引。 index 是一个K维度的张量,它可以认为是从 x 中取K-1维张量,每一个元素是一个切片:

\[output[(i_0, ..., i_{K-2})] = x[index[(i_0, ..., i_{K-2})]]\]

显然, index.shape[-1] <= x.rank 并且输出张量的维度是 index.shape[:-1] + x.shape[index.shape[-1]:]

示例:

给定:
    x = [[[ 0,  1,  2,  3],
              [ 4,  5,  6,  7],
              [ 8,  9, 10, 11]],
             [[12, 13, 14, 15],
              [16, 17, 18, 19],
              [20, 21, 22, 23]]]
    x.shape = (2, 3, 4)

- 案例 1:
    index = [[1]]

    gather_nd(x, index)
             = [x[1, :, :]]
             = [[12, 13, 14, 15],
                [16, 17, 18, 19],
                [20, 21, 22, 23]]

- 案例 2:

    index = [[0,2]]
    gather_nd(x, index)
             = [x[0, 2, :]]
             = [8, 9, 10, 11]

- 案例 3:

    index = [[1, 2, 3]]
    gather_nd(x, index)
             = [x[1, 2, 3]]
             = [23]
参数:
  • x (Tensor) - 输入Tensor,数据类型可以是int32,int64,float32,float64, bool。

  • index (Tensor) - 输入的索引Tensor,其数据类型int32或者int64。它的维度 index.rank 必须大于1,并且 index.shape[-1] <= x.rank

  • name (str,可选)- 具体用法请参见 Name ,一般无需设置,默认值为None。

返回:shape为index.shape[:-1] + x.shape[index.shape[-1]:]的Tensor,数据类型与 x 一致。

代码示例

import paddle
import numpy as np

np_x = np.array([[[1, 2], [3, 4], [5, 6]],
                 [[7, 8], [9, 10], [11, 12]]])
np_index = [[0, 1]]
x = paddle.to_tensor(np_x)
index = paddle.to_tensor(np_index)

output = paddle.gather_nd(x, index) #[[3, 4]]