A 1D adaptive average pooling over an input signal composed of several input planes, based on output_size. Input and output are in NCL format, where N is batch size, C is the number of channels and L is the length of the feature. The shape of output will be $$[N, C, output\_size]$$.

The formulation for average adaptive pool1d is

\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}
Parameters
• output_size (int) – The target output size. Its data type must be int.

• name (str, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Returns

A callable object for computing 1D adaptive average pooling.

Examples

# 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)
#

data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
# pool_out shape: [1, 3, 16]

forward ( input )

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments

extra_repr ( )

Extra representation of this layer, you can have custom implementation of your own layer.