AdaptiveMaxPool1D¶

class paddle.nn. AdaptiveMaxPool1D ( output_size, return_mask=False, name=None ) [source]

This operation applies a 1D adaptive max pooling over an input signal composed of several input planes, based on the input, output_size, return_mask parameters. Input(X) and output(Out) are in NCL format, where N is batch size, C is the number of channels, L is the length of the feature. The output tensor shape will be [N, C, output_size].

For max adaptive pool1d:

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

Parameters

output_size (int|list|tuple) – The pool kernel size. If pool kernel size is a tuple or list, it must contain one int.
return_mask (bool, optional) – If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False.
name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Returns

A callable object of AdaptiveMaxPool1D.

Shape:

x(Tensor): The input tensor of adaptive max pool1d operator, which is a 3-D tensor. The data type can be float32, float64.
output(Tensor): The output tensor of adaptive max pool1d operator, which is a 3-D tensor. The data type is same as input x.

Examples

           >>> # max 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] = max(input[:, :, lstart: lend])
>>> #
>>> import paddle
>>> import paddle.nn as nn

>>> data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
>>> AdaptiveMaxPool1D = nn.AdaptiveMaxPool1D(output_size=16)
>>> pool_out = AdaptiveMaxPool1D(data)
>>> print(pool_out.shape)
[1, 3, 16]

>>> # for return_mask = true
>>> AdaptiveMaxPool1D = nn.AdaptiveMaxPool1D(output_size=16, return_mask=True)
>>> pool_out, indices = AdaptiveMaxPool1D(data)
>>> print(pool_out.shape)
[1, 3, 16]
>>> print(indices.shape)
[1, 3, 16]

          

forward ( input ) forward¶

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

AdaptiveMaxPool1D¶

forward¶

extra_repr¶