AdaptiveAvgPool1D¶
该算子根据输入 x , output_size 等参数对一个输入Tensor计算1D的自适应平均池化。输入和输出都是3-D Tensor， 默认是以 NCL 格式表示的，其中 N 是 batch size, C 是通道数, L 是输入特征的长度.
计算公式如下:
\[ \begin{align}\begin{aligned}lstart &= floor(i * L_{in} / L_{out})\\lend &= ceil((i + 1) * L_{in} / L_{out})\\Output(i) &= \frac{\sum Input[lstart:lend]}{lend - lstart}\end{aligned}\end{align} \]
形状¶
x (Tensor): 默认形状为（批大小，通道数，输出特征长度），即NCL格式的3-D Tensor。 其数据类型为float32或者float64。
output (Tensor): 默认形状为（批大小，通道数，输出特征长度），即NCL格式的3-D Tensor。 其数据类型与输入x相同。
返回¶
计算AdaptiveAvgPool1D的可调用对象
抛出异常¶
ValueError-output_size应是一个整数。
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 avg 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.to_tensor(paddle.uniform(shape = [1, 3, 32], min = -1, max = 1, dtype = "float32"))
AdaptiveAvgPool1D = nn.layer.AdaptiveAvgPool1D(output_size=16)
pool_out = AdaptiveAvgPool1D(data)
# pool_out shape: [1, 3, 16]