AdaptiveAvgPool1D

paddle.nn. AdaptiveAvgPool1D ( output_size, name=None ) [源代码]

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:

\[ \begin{align}\begin{aligned}lstart &= floor(i * L_{in} / L_{out})\\lend &= ceil((i + 1) * L_{in} / L_{out})\\Output(i) &= \frac{sum(Input[lstart:lend])}{(lstart - lend)}\end{aligned}\end{align} \]

参数

  • output_size (int): 算子输出特征图的长度,其数据类型为int。

  • name (str,可选): 操作的名称(可选,默认值为None)。更多信息请参见 Name

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])/(lstart - lend)
#
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]