mindspore.ops.Gather

class mindspore.ops.Gather

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 。

更多参考详见 mindspore.ops.gather()。

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, ). 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, ). 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.]]