mindspore.ops.ScatterSub
- class mindspore.ops.ScatterSub(use_locking=False)[源代码]
使用给定更新值通过减法操作和输入索引来更新Tensor值。此操作在更新完成后输出数据 ,这有利于更加方便地使用更新后的值。
对于每个在 indices.shape 中的 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。如果它们具有不同的数据类型,则优先级低的数据类型将转换为优先级相对最高的数据类型。当需要转换Parameter的数据类型时,会抛出RuntimeError异常。
- 参数:
use_locking (bool) - 表示是否使用锁来保护。默认值:
False
。
- 输入:
input_x (Parameter) - ScatterSub的输入,数据类型为Parameter。其shape为 \((N, *)\) ,其中 \(*\) 为任意数量的额外维度。
indices (Tensor) - 指定相减操作的索引,其数据类型必须为mindspore.int32。
updates (Tensor) - 指定与 input_x 相减的Tensor,其数据类型与 input_x 的相同,shape为 indices_shape + x_shape[1:] 。
- 输出:
Tensor,表示更新后的 input_x ,其shape和数据类型与 input_x 的相同。
- 异常:
TypeError - use_locking 不是bool。
TypeError - indices 不是int32。
ValueError - updates 的shape不是 indices_shape + 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。
- 支持平台:
Ascend
GPU
CPU
样例:
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops, Parameter >>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]), mindspore.float32), name="x") >>> indices = Tensor(np.array([[0, 1]]), mindspore.int32) >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]]), mindspore.float32) >>> scatter_sub = ops.ScatterSub() >>> output = scatter_sub(input_x, indices, updates) >>> print(output) [[-1. -1. -1.] [-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([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="x") >>> # for indices = [[0, 1], [1, 1]] >>> # step 1: [0, 1] >>> # input_x[0] = [0.0, 0.0, 0.0] - [1.0, 1.0, 1.0] = [-1.0, -1.0, -1.0] >>> # input_x[1] = [0.0, 0.0, 0.0] - [3.0, 3.0, 3.0] = [-3.0, -3.0, -3.0] >>> # step 2: [1, 1] >>> # input_x[1] = [-3.0, -3.0, -3.0] - [7.0, 7.0, 7.0] = [-10.0, -10.0, -10.0] >>> # input_x[1] = [-10.0, -10.0, -10.0] - [9.0, 9.0, 9.0] = [-19.0, -19.0, -19.0] >>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32) >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]], ... [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]]), mindspore.float32) >>> scatter_sub = ops.ScatterSub() >>> output = scatter_sub(input_x, indices, updates) >>> print(output) [[ -1. -1. -1.] [-19. -19. -19.]] >>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized. >>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="x") >>> # for indices = [[1, 0], [1, 1]] >>> # step 1: [1, 0] >>> # input_x[0] = [0.0, 0.0, 0.0] - [3.0, 3.0, 3.0] = [-3.0, -3.0, -3.0] >>> # input_x[1] = [0.0, 0.0, 0.0] - [1.0, 1.0, 1.0] = [-1.0, -1.0, -1.0] >>> # step 2: [1, 1] >>> # input_x[1] = [-1.0, -1.0, -1.0] - [7.0, 7.0, 7.0] = [-8.0, -8.0, -8.0] >>> # input_x[1] = [-8.0, -8.0, -8.0] - [9.0, 9.0, 9.0] = [-17.0, -17.0, -17.0] >>> indices = Tensor(np.array([[1, 0], [1, 1]]), mindspore.int32) >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]], ... [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]]), mindspore.float32) >>> scatter_sub = ops.ScatterSub() >>> output = scatter_sub(input_x, indices, updates) >>> print(output) [[ -3. -3. -3.] [-17. -17. -17.]] >>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized. >>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="x") >>> # for indices = [[0, 1], [0, 1]] >>> # step 1: [0, 1] >>> # input_x[0] = [0.0, 0.0, 0.0] - [1.0, 1.0, 1.0] = [-1.0, -1.0, -1.0] >>> # input_x[1] = [0.0, 0.0, 0.0] - [3.0, 3.0, 3.0] = [-3.0, -3.0, -3.0] >>> # step 2: [0, 1] >>> # input_x[0] = [-1.0, -1.0, -1.0] - [7.0, 7.0, 7.0] = [-8.0, -8.0, -8.0] >>> # input_x[1] = [-3.0, -3.0, -3.0] - [9.0, 9.0, 9.0] = [-12.0, -12.0, -12.0] >>> indices = Tensor(np.array([[0, 1], [0, 1]]), mindspore.int32) >>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]], ... [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]]), mindspore.float32) >>> scatter_sub = ops.ScatterSub() >>> output = scatter_sub(input_x, indices, updates) >>> print(output) [[ -8. -8. -8.] [-12. -12. -12.]]