mindspore.ops.ScatterDiv
- class mindspore.ops.ScatterDiv(use_locking=False)[源代码]
通过除法操作更新输入张量的值。
根据指定更新值和输入索引通过除法操作更新输入数据的值。 该操作在更新完成后输出 input_x ,这样方便使用更新后的值。
对于 indices.shape 的每个 \(i, ..., j\) :
\[\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{/}= \text{updates}[i, ..., j, :]\]输入的 input_x 和 updates 遵循隐式类型转换规则,以确保数据类型一致。如果数据类型不同,则低精度数据类型将转换为高精度的数据类型。当 updates 不支持转成 input_x 需要的数据类型时,则会抛出RuntimeError异常。
- 参数:
use_locking (bool) - 是否启用锁保护。默认值:
False
。
- 输入:
input_x (Parameter) - ScatterDiv的输入,任意维度的Parameter。shape: \((N, *)\) ,其中 \(*\) 表示任意数量的附加维度。
indices (Tensor) - 指定相除操作的索引,数据类型必须为mindspore.int32或者mindspore.int64。
updates (Tensor) - 指定与 input_x 相除的Tensor,数据类型与 input_x 相同,shape为 indices.shape + input_x.shape[1:] 。
- 输出:
Tensor,更新后的 input_x ,shape和类型与 input_x 相同。
- 异常:
TypeError - use_locking 不是bool。
TypeError - indices 不是int32或者int64。
ValueError - updates 的shape不等于 indices.shape + input_x.shape[1:] 。
RuntimeError - 当 input_x 和 updates 类型不一致,需要进行类型转换时,如果 updates 不支持转成参数 input_x 需要的数据类型,就会报错。
RuntimeError - 在Ascend平台上,输入的 input_x , indices 和 updates 的数据维度大于八维。
- 支持平台:
Ascend
GPU
CPU
样例:
>>> import numpy as np >>> from mindspore import dtype as mstype >>> from mindspore import Tensor, ops, Parameter >>> input_x = Parameter(Tensor(np.array([[6.0, 6.0, 6.0], [2.0, 2.0, 2.0]]), mstype.float32), name="x") >>> indices = Tensor(np.array([0, 1]), mstype.int32) >>> updates = Tensor(np.array([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]), mstype.float32) >>> scatter_div = ops.ScatterDiv() >>> output = scatter_div(input_x, indices, updates) >>> print(output) [[3. 3. 3.] [1. 1. 1.]] >>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized. >>> input_x = Parameter(Tensor(np.array([[105.0, 105.0, 105.0], ... [315.0, 315.0, 315.0]]), mstype.float32), name="x") >>> # for indices = [[0, 1], [1, 1]] >>> # step 1: [0, 1] >>> # input_x[0] = [105.0, 105.0, 105.0] / [1.0, 1.0, 1.0] = [105.0, 105.0, 105.0] >>> # input_x[1] = [315.0, 315.0, 315.0] / [3.0, 3.0, 3.0] = [105.0, 105.0, 105.0] >>> # step 2: [1, 1] >>> # input_x[1] = [105.0, 105.0, 105.0] / [5.0, 5.0, 5.0] = [21.0, 21.0, 21.0] >>> # input_x[1] = [21.0, 21.0, 21.0] / [7.0, 7.0, 7.0] = [3.0, 3.0, 3.0] >>> indices = Tensor(np.array([[0, 1], [1, 1]]), mstype.int32) >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]], ... [[5.0, 5.0, 5.0], [7.0, 7.0, 7.0]]]), mstype.float32) >>> scatter_div = ops.ScatterDiv() >>> output = scatter_div(input_x, indices, updates) >>> print(output) [[105. 105. 105.] [ 3. 3. 3.]] >>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized. >>> input_x = Parameter(Tensor(np.array([[105.0, 105.0, 105.0], ... [315.0, 315.0, 315.0]]), mstype.float32), name="x") >>> # for indices = [[1, 0], [1, 1]] >>> # step 1: [1, 0] >>> # input_x[0] = [105.0, 105.0, 105.0] / [3.0, 3.0, 3.0] = [35.0, 35.0, 35.0] >>> # input_x[1] = [315.0, 315.0, 315.0] / [1.0, 1.0, 1.0] = [315.0, 315.0, 315.0] >>> # step 2: [1, 1] >>> # input_x[1] = [315.0, 315.0, 315.0] / [5.0, 5.0, 5.0] = [63.0 63.0 63.0] >>> # input_x[1] = [63.0 63.0 63.0] / [7.0, 7.0, 7.0] = [9.0, 9.0, 9.0] >>> indices = Tensor(np.array([[1, 0], [1, 1]]), mstype.int32) >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]], ... [[5.0, 5.0, 5.0], [7.0, 7.0, 7.0]]]), mstype.float32) >>> scatter_div = ops.ScatterDiv() >>> output = scatter_div(input_x, indices, updates) >>> print(output) [[35. 35. 35.] [ 9. 9. 9.]] >>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized. >>> input_x = Parameter(Tensor(np.array([[105.0, 105.0, 105.0], ... [315.0, 315.0, 315.0]]), mstype.float32), name="x") >>> # for indices = [[0, 1], [0, 1]] >>> # step 1: [0, 1] >>> # input_x[0] = [105.0, 105.0, 105.0] / [1.0, 1.0, 1.0] = [105.0, 105.0, 105.0] >>> # input_x[1] = [315.0, 315.0, 315.0] / [3.0, 3.0, 3.0] = [105.0, 105.0, 105.0] >>> # step 2: [0, 1] >>> # input_x[0] = [105.0, 105.0, 105.0] / [5.0, 5.0, 5.0] = [21.0, 21.0, 21.0] >>> # input_x[1] = [105.0, 105.0, 105.0] / [7.0, 7.0, 7.0] = [15.0, 15.0, 15.0] >>> indices = Tensor(np.array([[0, 1], [0, 1]]), mstype.int32) >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]], ... [[5.0, 5.0, 5.0], [7.0, 7.0, 7.0]]]), mstype.float32) >>> scatter_div = ops.ScatterDiv() >>> output = scatter_div(input_x, indices, updates) >>> print(output) [[21. 21. 21.] [15. 15. 15.]]