mindspore.ops.gather

mindspore.ops.gather(input_params, input_indices, axis)

返回输入Tensor在指定 axis 上 input_indices 索引对应的元素组成的切片。

下图展示了Gather常用的计算过程：

其中，params代表输入 input_params ，indices代表要切片的索引 input_indices 。

说明

1. input_indices的值必须在 [0, input_param.shape[axis]) 范围内，超出该范围结果未定义。

2. Ascend平台上，input_params的数据类型当前不能是 bool_ 。

参数：

• input_params (Tensor) - 原始Tensor，shape为 \((x_1, x_2, ..., x_R)\) 。

• input_indices (Tensor) - 要切片的索引Tensor，shape为 \((y_1, y_2, ..., y_S)\) 。指定原始Tensor中要切片的索引。数据类型必须是int32或int64。

• axis (int) - 指定要切片的维度索引。

返回：

Tensor，shape为 \(input\_params.shape[:axis] + input\_indices.shape + input\_params.shape[axis + 1:]\) 。

异常：

• TypeError - axis 不是int。

• TypeError - input_params 不是Tensor。

• TypeError - input_indices 不是int类型的Tensor。

支持平台：

Ascend GPU CPU

样例：

>>> # case1: input_indices is a Tensor with shape (5, ).
>>> input_params = Tensor(np.array([1, 2, 3, 4, 5, 6, 7]), mindspore.float32)
>>> input_indices = Tensor(np.array([0, 2, 4, 2, 6]), mindspore.int32)
>>> axis = 0
>>> output = ops.gather(input_params, input_indices, axis)
>>> print(output)
[1. 3. 5. 3. 7.]
>>> # case2: input_indices is a Tensor with shape (2, 2). When the input_params has one dimension,
>>> # the output shape is equal to the input_indices shape.
>>> input_indices = Tensor(np.array([[0, 2], [2, 6]]), mindspore.int32)
>>> axis = 0
>>> output = ops.gather(input_params, input_indices, axis)
>>> print(output)
[[ 1. 3.]
 [ 3. 7.]]
>>> # case3: input_indices is a Tensor with shape (2, ) and
>>> # input_params is a Tensor with shape (3, 4) and axis is 0.
>>> input_params = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), mindspore.float32)
>>> input_indices = Tensor(np.array([0, 2]), mindspore.int32)
>>> axis = 0
>>> output = ops.gather(input_params, input_indices, axis)
>>> print(output)
[[1.  2.  3.  4.]
 [9. 10. 11. 12.]]
>>> # case4: input_indices is a Tensor with shape (2, ) and
>>> # input_params is a Tensor with shape (3, 4) and axis is 1.
>>> input_params = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), mindspore.float32)
>>> input_indices = Tensor(np.array([0, 2]), mindspore.int32)
>>> axis = 1
>>> output = ops.gather(input_params, input_indices, axis)
>>> print(output)
[[1.  3.]
 [5.  7.]
 [9. 11.]]