scatter¶

paddle. scatter ( x, index, updates, overwrite=True, name=None ) [源代码] ¶

通过基于 updates 来更新选定索引 index 上的输入来获得输出。具体行为如下：

import numpy as np
#input:
x = np.array([[1, 1], [2, 2], [3, 3]])
index = np.array([2, 1, 0, 1])
# shape of updates should be the same as x
# shape of updates with dim > 1 should be the same as input
updates = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
overwrite = False
# calculation:
if not overwrite:
    for i in range(len(index)):
        x[index[i]] = np.zeros((2))
for i in range(len(index)):
    if (overwrite):
        x[index[i]] = updates[i]
    else:
        x[index[i]] += updates[i]
# output:
out = np.array([[3, 3], [6, 6], [1, 1]])
out.shape # [3, 2]

Notice： 因为 updates 的应用顺序是不确定的，因此，如果索引 index 包含重复项，则输出将具有不确定性。

import paddle
import numpy as np

x_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float32)
index_data = np.array([2, 1, 0, 1]).astype(np.int64)
updates_data = np.array([[1, 1], [2, 2], [3, 3], [4, 4]]).astype(np.float32)

x = paddle.to_tensor(x_data)
index = paddle.to_tensor(index_data)
updates = paddle.to_tensor(updates_data)

output1 = paddle.scatter(x, index, updates, overwrite=False)
# [[3., 3.],
#  [6., 6.],
#  [1., 1.]]
output2 = paddle.scatter(x, index, updates, overwrite=True)
# CPU device:
# [[3., 3.],
#  [4., 4.],
#  [1., 1.]]
# GPU device maybe have two results because of the repeated numbers in index
# result 1:
# [[3., 3.],
#  [4., 4.],
#  [1., 1.]]
# result 2:
# [[3., 3.],
#  [2., 2.],
#  [1., 1.]]