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paddle.fluid / layers / sequence_expand
sequence_expand¶
序列扩张层(Sequence Expand Layer),根据输入 y
的第 ref_level
层lod对输入 x
进行扩展。 x
的lod level最多为1,若 x
的lod level为1,则 x
的lod大小必须与 y
的第 ref_level
层lod大小相等;若 x
的lod level为0,则 x
的第一维大小必须与 y
第 ref_level
层大小相等。 x
的秩最少为2,当 x
的秩大于2时,将被当作是一个二维张量处理。
注意,该OP的输入 x
可以是Tensor或LodTensor, y
只能是LodTensor。
范例解释如下:
例1:
假设两个长度为2的序列[a][b]和[c][d],欲将其扩展为4个长度为2的序列[a][b]、[a][b]、[c][d]、[c][d]。
序列[a][b]扩展2次,[c][d]扩展2次,扩展所需依据的lod为[2, 2],则:
给定输入一维LoDTensor x
x.lod = [[2, 2]] #表示两个序列的长度为2,为了便于理解这里用基于长度lod表示
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
和输入 y
y.lod = [[2, 2], #第0层lod,指定按该层扩展,表示分别扩展2次,为了便于理解这里用基于长度lod表示
[3, 3, 1, 1]] #第1层lod,注意,因为指定ref_level为0,所以这一层与运算无关
指定 ref_level = 0,依据y的第0层lod进行扩展,
经过sequence_expand,输出为1级LoDTensor out
out.lod = [[0, 2, 4, 6, 8]] #基于偏移的lod,等价于基于长度的[[2, 2, 2, 2]]
out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
out.dims = [8, 1]
例2:
假设有3个长度维1的序列[a]、[b]、[c],现在要将其扩展为长度是2、0、3的序列[a][a]、[c][c][c]。
显然,扩展后的序列lod为[2, 0, 3],则:
给定输入一维LoDTensor x
x.data = [[a], [b], [c]]
x.dims = [3, 1]
和输入 y
y.lod = [[2, 0, 3]]
默认 ref_level = -1
经过sequence_expand,输出为1级LoDTensor out
out.data = [[a], [a], [c], [c], [c]]
out.dims = [5, 1]
- 参数:
-
x (Variable) - 输入变量,维度为 \([M, K]\) ,lod level至多1的二维Tensor或LoDTensor。数据类型支持int32,int64,float32或float64。
y (Variable) - 输入变量,lod level至少为1的LoDTensor。数据类型不限。
ref_level (int,可选) - 扩展
x
所依据的y
的lod层。默认值-1,表示lod的最后一层。name (str,可选) - 具体用法请参见 Name ,一般无需设置,默认值为None。
返回:扩展变量,维度为 \([N, K]\) 的LoDTensor,N由输入 x
和 y
的lod共同决定。数据类型与输入 x
一致。
返回类型:Variable
代码示例:
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
x = fluid.data(name='x', shape=[1], dtype='float32')
y = fluid.data(name='y', shape=[1],
dtype='float32', lod_level=1)
out = layers.sequence_expand(x=x, y=y, ref_level=0)
exe = fluid.Executor(fluid.CPUPlace())
place = fluid.CPUPlace()
np_data = np.array([[1], [2], [3], [4]]).astype('float32')
x_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2]], place)
print(x_lod_tensor)
#lod: [[0, 2, 4]]
# dim: 4, 1
# layout: NCHW
# dtype: float
# data: [1 2 3 4]
y_lod_tensor = fluid.create_random_int_lodtensor([[2, 2], [3,3,1,1]], [1],
place, low=0, high=1)
print(y_lod_tensor)
#lod: [[0, 2, 4][0, 3, 6, 7, 8]]
# dim: 8, 1
# layout: NCHW
# dtype: int64_t
# data: [0 0 1 1 1 1 1 0]
out_main = exe.run(fluid.default_main_program(),
feed={'x': x_lod_tensor, 'y': y_lod_tensor},
fetch_list=[out], return_numpy=False)
print(out_main[0])
#lod: [[0, 2, 4, 6, 8]]
# dim: 8, 1
# layout: NCHW
# dtype: float
# data: [1 2 1 2 3 4 3 4]
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