AdaptiveAvgPool1D¶
根据 output_size
对一个输入 Tensor 计算 1D 的自适应平均池化。输入和输出都是以 NCL 格式表示的 3-D Tensor,其中 N 是批大小,C 是通道数而 L 是特征的长度。输出的形状是 \([N, C, output\_size]\)。
计算公式为
\[ \begin{align}\begin{aligned}lstart &= \lfloor i * L_{in} / L_{out}\rfloor,\\lend &= \lceil(i + 1) * L_{in} / L_{out}\rceil,\\Output(i) &= \frac{\sum Input[lstart:lend]}{lend - lstart}.\end{aligned}\end{align} \]
返回¶
用于计算 1D 自适应平均池化的可调用对象。
代码示例¶
# average adaptive pool1d
# suppose input data in shape of [N, C, L], `output_size` is m or [m],
# output shape is [N, C, m], adaptive pool divide L dimension
# of input data into m grids averagely and performs poolings in each
# grid to get output.
# adaptive max pool performs calculations as follow:
#
# for i in range(m):
# lstart = floor(i * L / m)
# lend = ceil((i + 1) * L / m)
# output[:, :, i] = sum(input[:, :, lstart: lend])/(lend - lstart)
#
import paddle
import paddle.nn as nn
data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
AdaptiveAvgPool1D = nn.AdaptiveAvgPool1D(output_size=16)
pool_out = AdaptiveAvgPool1D(data)
# pool_out shape: [1, 3, 16]