paddlepaddle--paddle/python/paddle/nn/layer/pooling.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


from __future__ import annotations

import warnings
from typing import TYPE_CHECKING

from paddle.utils.decorator_utils import (
    lp_pool_layer_decorator,
    maxpool_layer_decorator,
    param_one_alias,
)

from .. import functional as F
from .layers import Layer

if TYPE_CHECKING:
    from collections.abc import Sequence

    from paddle import Tensor
    from paddle._typing import (
        DataLayout1D,
        DataLayout2D,
        DataLayout3D,
        Size1,
        Size2,
        Size3,
        Size4,
        Size6,
    )

    from ..functional.common import _PaddingSizeMode

__all__ = []


class AvgPool1D(Layer):
    r"""
    This operation applies a 1D average 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].

    The output value of the layer with input size (N, C, L),
    output (N, C, :math:`L_{out}`) and kernel_size ksize can be precisely described as
    For average pool1d:

    ..  math::

        Output(N_i, C_i, l) = \frac{Input[N_i, C_i, stride \times l:stride \times l+k]}{ksize}

    Parameters:
        kernel_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain an integer.
        stride(int|list|tuple|None, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain an integer. Default None, then stride will be equal to the kernel_size.
        padding(str|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
            1. A string in ['valid', 'same'].
            2. An int, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 1, which means the feature map is zero padded by the size of `padding[0]` on every sides.
            4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after].
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
            The default value is 0.
        exclusive(bool, optional): Whether to exclude padding points in average pooling mode, default is `True`.
        ceil_mode(bool, optional): ${ceil_mode_comment}Whether to use the ceil function to calculate output height
            and width. If it is set to False, the floor function will be used. The default value is False.
        name(str|None, optional): For eed to detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no nset and None by default.

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

    Returns:
        A callable object of AvgPool1D.

    Examples:

        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
            >>> avg_pool1d = nn.AvgPool1D(kernel_size=2, stride=2, padding=0)
            >>> pool_out = avg_pool1d(data)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 16])

    """

    kernel_size: Size1
    stride: Size1 | None
    padding: _PaddingSizeMode | Size1 | Size2
    exclusive: bool
    ceil_mode: bool
    name: str | None

    def __init__(
        self,
        kernel_size: Size1,
        stride: Size1 | None = None,
        padding: _PaddingSizeMode | Size1 | Size2 = 0,
        exclusive: bool = True,
        ceil_mode: bool = False,
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.ceil_mode = ceil_mode
        self.exclusive = exclusive
        self.name = name

    def forward(self, x: Tensor) -> Tensor:
        out = F.avg_pool1d(
            x,
            self.kernel_size,
            self.stride,
            self.padding,
            self.exclusive,
            self.ceil_mode,
            self.name,
        )
        return out

    def extra_repr(self) -> str:
        return 'kernel_size={kernel_size}, stride={stride}, padding={padding}'.format(
            **self.__dict__
        )


class AvgPool2D(Layer):
    r"""
    This operation applies 2D average pooling over input features based on the input,
    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
    in NCHW format, where N is batch size, C is the number of channels,
    H is the height of the feature, and W is the width of the feature.

    Example:
        Input:
            X shape: :math:`(N, C, :math:`H_{in}`, :math:`W_{in}`)`
        Attr:
            kernel_size: ksize

        Output:
            Out shape: :math:`(N, C, :math:`H_{out}`, :math:`W_{out}`)`

        ..  math::

            Output(N_i, C_j, h, w)  = \frac{\sum_{m=0}^{ksize[0]-1} \sum_{n=0}^{ksize[1]-1}
                Input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)}{ksize[0] * ksize[1]}


    Parameters:
        kernel_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain two integers, (pool_size_Height, pool_size_Width).
            Otherwise, the pool kernel size will be a square of an int.
        stride(int|list|tuple|None, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain two integers, (pool_stride_Height, pool_stride_Width).
            Otherwise, the pool stride size will be a square of an int.
            Default None, then stride will be equal to the kernel_size.
        padding(str|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
            1. A string in ['valid', 'same'].
            2. An int, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 2, [pad_height, pad_weight] whose value means the padding size of each dimension.
            4. A list[int] or tuple(int) whose length is 4. [pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side.
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
            The default value is 0.
        ceil_mode(bool, optional): When True, will use `ceil` instead of `floor` to compute the output shape.
        exclusive(bool, optional): Whether to exclude padding points in average pooling
            mode, default is `true`.
        divisor_override(float, optional): If specified, it will be used as divisor, otherwise kernel_size will be
            used. Default None.
        data_format(str, optional): The data format of the input and output data. An optional string from: `"NCHW"`,
            `"NHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_height, input_width]`.
        name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.

    Shape:
        - x(Tensor): The input tensor of avg pool2d operator, which is a 4-D tensor.
          The data type can be float32, float64.
        - output(Tensor): The output tensor of avg pool2d  operator, which is a 4-D tensor.
          The data type is same as input x.

    Returns:
        A callable object of AvgPool2D.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> # avg pool2d
            >>> input = paddle.uniform([1, 3, 32, 32], dtype="float32", min=-1, max=1)
            >>> avg_pool2d = nn.AvgPool2D(kernel_size=2, stride=2, padding=0)
            >>> output = avg_pool2d(input)
            >>> print(output.shape)
            paddle.Size([1, 3, 16, 16])

    """

    kernel_size: Size2
    stride: Size2 | None
    padding: _PaddingSizeMode | Size2 | Size4
    ceil_mode: bool
    exclusive: bool
    divisor: float | None
    data_format: DataLayout2D
    name: str | None

    def __init__(
        self,
        kernel_size: Size2,
        stride: Size2 | None = None,
        padding: _PaddingSizeMode | Size2 | Size4 = 0,
        ceil_mode: bool = False,
        exclusive: bool = True,
        divisor_override: float | None = None,
        data_format: DataLayout2D = 'NCHW',
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.ksize = kernel_size
        self.stride = stride
        self.padding = padding
        self.ceil_mode = ceil_mode
        self.exclusive = exclusive
        self.divisor = divisor_override
        self.data_format = data_format
        self.name = name

    def forward(self, x):
        return F.avg_pool2d(
            x,
            kernel_size=self.ksize,
            stride=self.stride,
            padding=self.padding,
            ceil_mode=self.ceil_mode,
            exclusive=self.exclusive,
            divisor_override=self.divisor,
            data_format=self.data_format,
            name=self.name,
        )

    def extra_repr(self) -> str:
        return 'kernel_size={ksize}, stride={stride}, padding={padding}'.format(
            **self.__dict__
        )


class AvgPool3D(Layer):
    """

    This operation applies 3D max pooling over input features based on the input,
    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
    in NCDHW format, where N is batch size, C is the number of channels,
    H is the height of the feature,  D is the depth of the feature, and W is the width of the feature.

    Parameters:
        kernel_size(int|list|tuple): The pool kernel size. If pool kernel size
            is a tuple or list, it must contain three integers,
            (kernel_size_Depth, kernel_size_Height, kernel_size_Width).
            Otherwise, the pool kernel size will be the cube of an int.
        stride(int|list|tuple|None, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain three integers, [stride_Depth, stride_Height, stride_Width).
            Otherwise, the pool stride size will be a cube of an int.
            Default None, then stride will be equal to the kernel_size.
        padding(str|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.

            1. A string in ['valid', 'same'].
            2. An int, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 3, [pad_depth, pad_height, pad_weight] whose value means the padding size of each dimension.
            4. A list[int] or tuple(int) whose length is 6. [pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side.
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).

            The default value is 0.
        ceil_mode(bool, optional): ${ceil_mode_comment}
        exclusive(bool, optional): Whether to exclude padding points in average pooling mode, default is True.
        divisor_override(int|float, optional): if specified, it will be used as divisor, otherwise kernel_size will
            be used. Default None.
        data_format(str, optional): The data format of the input and output data. An optional string from: `"NCDHW"`,
             `"NDHWC"`. The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
             `[batch_size, input_channels, input_depth, input_height, input_width]`.
        name(str|None, optional): For detailed information, please refer
             to :ref:`api_guide_Name`. Usually name is no need to set and
             None by default.

    Returns:
        A callable object of AvgPool3D.

    Shape:
        - x(Tensor): The input tensor of avg pool3d operator, which is a 5-D tensor.
          The data type can be float16, float32, float64.
        - output(Tensor): The output tensor of avg pool3d  operator, which is a 5-D tensor.
          The data type is same as input x.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> # avg pool3d
            >>> input = paddle.uniform([1, 2, 3, 32, 32], dtype="float32", min=-1, max=1)
            >>> avg_pool3d = nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
            >>> output = avg_pool3d(input)
            >>> print(output.shape)
            paddle.Size([1, 2, 1, 16, 16])

    """

    kernel_size: Size3
    stride: Size3 | None
    padding: _PaddingSizeMode | Size3 | Size6
    ceil_mode: bool
    exclusive: bool
    divisor: float | None
    data_format: DataLayout3D
    name: str | None

    def __init__(
        self,
        kernel_size: Size3,
        stride: Size3 | None = None,
        padding: _PaddingSizeMode | Size3 | Size6 = 0,
        ceil_mode: bool = False,
        exclusive: bool = True,
        divisor_override: float | None = None,
        data_format: DataLayout3D = 'NCDHW',
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.ksize = kernel_size
        self.stride = stride
        self.padding = padding
        self.ceil_mode = ceil_mode
        self.exclusive = exclusive
        self.divisor = divisor_override
        self.data_format = data_format
        self.name = name

    def forward(self, x: Tensor) -> Tensor:
        return F.avg_pool3d(
            x,
            kernel_size=self.ksize,
            stride=self.stride,
            padding=self.padding,
            ceil_mode=self.ceil_mode,
            exclusive=self.exclusive,
            divisor_override=self.divisor,
            data_format=self.data_format,
            name=self.name,
        )

    def extra_repr(self) -> str:
        return 'kernel_size={ksize}, stride={stride}, padding={padding}'.format(
            **self.__dict__
        )


class LPPool1D(Layer):
    r"""
    Performing a 1D power-average 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].

    The output value of the layer with input size (N, C, L),
    output (N, C, :math:`L_{out}`) and kernel_size ksize can be precisely described as
    For average pool1d:

    ..  math::

        Output(N_i, C_i, l) = sum(Input[N_i, C_i, stride \times l:stride \times l+k]^{norm\_type})^{1/norm\_type}

    Parameters:
        norm_type(int|float): The number the power operation.
        kernel_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain an integer.
        stride(int|list|tuple|None, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain an integer. Default None, then stride will be equal to the kernel_size.
        padding(str|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
            1. A string in ['valid', 'same'].
            2. An int, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 1, which means the feature map is zero padded by the size of `padding[0]` on every sides.
            4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after].
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
            The default value is 0.
        ceil_mode(bool, optional): When True, it will use `ceil` instead of `floor` to compute the output shape. Default: False.
        data_format(str, optional): The data format of the input and output data. An optional string from: `"NCL"`,
            `"NLC"`. When it is `"NCL"`, the data is stored in the order of:
            `[batch_size, input_channels, input_length]`. Default: "NCL"
        name(str|None, optional): For eed to detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no nset and None by default.

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

    Returns:
        A callable object of LPPool1D.

    Examples:

        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
            >>> lp_pool1d = nn.LPPool1D(norm_type=2, kernel_size=2, stride=2, padding=0)
            >>> pool_out = lp_pool1d(data)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 16])

    """

    norm_type: float
    kernel_size: Size1
    stride: Size1 | None
    padding: _PaddingSizeMode | Size1 | Size2
    ceil_mode: bool
    data_format: DataLayout1D
    name: str | None

    @lp_pool_layer_decorator
    def __init__(
        self,
        norm_type: float,
        kernel_size: Size1,
        stride: Size1 | None = None,
        padding: _PaddingSizeMode | Size1 | Size2 = 0,
        ceil_mode: bool = False,
        data_format: DataLayout1D = "NCL",
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.norm_type = float(norm_type)
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.ceil_mode = ceil_mode
        self.data_format = data_format
        self.name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        out = F.lp_pool1d(
            x,
            self.norm_type,
            self.kernel_size,
            self.stride,
            self.padding,
            self.ceil_mode,
            self.data_format,
            self.name,
        )
        return out

    def extra_repr(self) -> str:
        return 'norm_type={norm_type}, kernel_size={kernel_size}, stride={stride}, padding={padding}'.format(
            **self.__dict__
        )


class LPPool2D(Layer):
    r"""
    Performing 2D power-average pooling over input features based on the input,
    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
    in NCHW format, where N is batch size, C is the number of channels,
    H is the height of the feature, and W is the width of the feature.

    Example:
        Input:
            X shape: :math:`(N, C, H_{in}, W_{in})`
        Attr:
            - kernel_size: kernel_size
            - norm_type: norm_type

        Output:
            Out shape: :math:`(N, C, H_{out}, W_{out})`

        ..  math::

            Output(N_i, C_j, h, w)  = (\sum_{m=0}^{ksize[0]-1} \sum_{n=0}^{ksize[1]-1}
                               Input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)^{norm\_type})^{1 / norm\_type}

    Parameters:
        norm_type(int|float): The number the power operation.
        kernel_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain two integers, (pool_size_Height, pool_size_Width).
            Otherwise, the pool kernel size will be a square of an int.
        stride(int|list|tuple, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain two integers, (pool_stride_Height, pool_stride_Width).
            Otherwise, the pool stride size will be a square of an int.
            Default None, then stride will be equal to the kernel_size.
        padding(str|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
            1. A string in ['valid', 'same'].
            2. An int, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 2, [pad_height, pad_weight] whose value means the padding size of each dimension.
            4. A list[int] or tuple(int) whose length is 4. [pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side.
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
            The default value is 0.
        ceil_mode(bool, optional): When True, it will use `ceil` instead of `floor` to compute the output shape. Default: False.
        data_format(str, optional): The data format of the input and output data. An optional string from: `"NCHW"`,
            `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_height, input_width]`. Default: "NCHW".
        name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.

    Shape:
        - x(Tensor): The input tensor of lp pool2d operator, which is a 4-D tensor.
          The data type can be float32, float64.
        - output(Tensor): The output tensor of lp pool2d  operator, which is a 4-D tensor.
          The data type is same as input x.

    Returns:
        A callable object of LPPool2D.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> # lp pool2d
            >>> input = paddle.uniform([1, 3, 32, 32], dtype="float32", min=-1, max=1)
            >>> lp_pool2d = nn.LPPool2D(norm_type=2, kernel_size=2, stride=2, padding=0)
            >>> output = lp_pool2d(input)
            >>> print(output.shape)
            paddle.Size([1, 3, 16, 16])

    """

    norm_type: float
    kernel_size: Size2
    stride: Size2 | None
    padding: _PaddingSizeMode | Size2 | Size4
    ceil_mode: bool
    data_format: DataLayout2D
    name: str | None

    @lp_pool_layer_decorator
    def __init__(
        self,
        norm_type: float,
        kernel_size: Size2,
        stride: Size2 | None = None,
        padding: _PaddingSizeMode | Size2 | Size4 = 0,
        ceil_mode: bool = False,
        data_format: DataLayout2D = "NCHW",
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.norm_type = float(norm_type)
        self.ksize = kernel_size
        self.stride = kernel_size if stride is None else stride
        self.padding = padding
        self.ceil_mode = ceil_mode
        self.data_format = data_format
        self.name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.lp_pool2d(
            x,
            norm_type=self.norm_type,
            kernel_size=self.ksize,
            stride=self.stride,
            padding=self.padding,
            ceil_mode=self.ceil_mode,
            data_format=self.data_format,
            name=self.name,
        )

    def extra_repr(self) -> str:
        return 'norm_type={norm_type}, kernel_size={ksize}, stride={stride}, padding={padding}'.format(
            **self.__dict__
        )


class MaxPool1D(Layer):
    """
    This operation applies 1D max pooling over input signal
    composed of several input planes based on the input,
    and kernel_size, stride, padding 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 value of the layer with input size (N, C, L),
    output (N, C, L_{out}) and kernel_size k can be precisely described as
    For average pool1d:

    ..  math::

        Output(N_i, C_i, l) =  max(Input[N_i, C_i, stride \times l:stride \times l+k])

    Parameters:
        kernel_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain an integer.
        stride(int|list|tuple|None, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain an integer. Default None, then stride will be equal to the kernel_size.
        padding(str|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
            1. A string in ['valid', 'same'].
            2. An integer, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 1, which means the feature map is zero padded by the size of `padding[0]` on every sides.
            4. A list[int] or tuple(int) whose length is 2, It has the form [pad_before, pad_after].
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or(0,0).
            The default value is 0.
        return_mask(bool, optional): Whether return the max indices along with the outputs. default is `False`.
        ceil_mode(bool, optional): Whether to use the ceil function to calculate output height and width.
            False is the default. If it is set to False, the floor function will be used. Default False.
        dilation(int|list|tuple, optional): The dilation size. If dilation size is a tuple or list,
            it must contain an integer. Default: 1.
        name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.
    Returns:
        A callable object of MaxPool1D.

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

    Examples:

        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
            >>> max_pool1d = nn.MaxPool1D(kernel_size=2, stride=2, padding=0)
            >>> pool_out = max_pool1d(data)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 16])

            >>> max_pool1d = nn.MaxPool1D(kernel_size=2, stride=2, padding=0, return_mask=True)
            >>> pool_out, indices = max_pool1d(data)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 16])
            >>> print(indices.shape)
            paddle.Size([1, 3, 16])

            >>> max_pool1d = nn.MaxPool1D(kernel_size=2, stride=2, padding=0, dilation=2)
            >>> pool_out = max_pool1d(data)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 15])
    """

    kernel_size: Size1
    stride: Size1 | None
    padding: _PaddingSizeMode | Size1 | Size2
    return_mask: bool
    ceil_mode: bool
    dilation: Size1
    name: str | None

    @maxpool_layer_decorator
    def __init__(
        self,
        kernel_size: Size1,
        stride: Size1 | None = None,
        padding: _PaddingSizeMode | Size1 | Size2 = 0,
        return_mask: bool = False,
        ceil_mode: bool = False,
        dilation: Size1 = 1,
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.ceil_mode = ceil_mode
        self.return_mask = return_mask
        self.dilation = dilation
        self.name = name

    def forward(self, input: Tensor) -> Tensor:
        out = F.max_pool1d(
            x=input,
            kernel_size=self.kernel_size,
            stride=self.stride,
            padding=self.padding,
            return_mask=self.return_mask,
            ceil_mode=self.ceil_mode,
            dilation=self.dilation,
            name=self.name,
        )
        return out

    def extra_repr(self) -> str:
        return 'kernel_size={kernel_size}, stride={stride}, padding={padding}, dilation={dilation}'.format(
            **self.__dict__
        )


class MaxPool2D(Layer):
    r"""
    This operation applies 2D max pooling over input feature based on the input,
    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
    in NCHW format, where N is batch size, C is the number of channels,
    H is the height of the feature, and W is the width of the feature.

    Example:
        - Input:
            X shape: :math:`(N, C, H_{in}, W_{in})`
        - Attr:
            kernel_size: ksize

        - Output:
            Out shape: :math:`(N, C, H_{out}, W_{out})`

        ..  math::

            Output(N_i, C_j, h, w) = \max_{m=0, \ldots, ksize[0] -1} \max_{n=0, \ldots, ksize[1]-1}
                Input(N_i, C_j, stride[0] \times h + m * dilation[0], stride[1] \times w + n * dilation[1])

    Parameters:
        kernel_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain two integers, (pool_size_Height, pool_size_Width).
            Otherwise, the pool kernel size will be a square of an int.
        stride(int|list|tuple|None, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain two integers, (pool_stride_Height, pool_stride_Width).
            Otherwise, the pool stride size will be a square of an int.
            Default None, then stride will be equal to the kernel_size.
        padding(str|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
            1. A string in ['valid', 'same'].
            2. An int, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 2, [pad_height, pad_weight] whose value means the padding size of each dimension.
            4. A list[int] or tuple(int) whose length is \4. [pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side.
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
            The default value is 0.
        return_mask(bool, optional): Whether to return the max indices along with the outputs.
        ceil_mode(bool, optional): when True, will use `ceil` instead of `floor` to compute the output shape
        dilation(int|list|tuple, optional): The dilation size. If dilation is a tuple or list, it must
            contain two integers, (dilation_Height, dilation_Width). Otherwise, the dilation size
            will be a square of an int. Default 1.
        data_format(str, optional): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
            The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_height, input_width]`.
        name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.

    Returns:
        A callable object of MaxPool2D.

    Shape:
        - x(Tensor): The input tensor of max pool2d operator, which is a 4-D tensor.
          The data type can be float32, float64.
        - output(Tensor): The output tensor of max pool2d  operator, which is a 4-D tensor.
          The data type is same as input x.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> # max pool2d
            >>> input = paddle.uniform([1, 3, 32, 32], dtype="float32", min=-1, max=1)
            >>> max_pool2d = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
            >>> output = max_pool2d(input)
            >>> print(output.shape)
            paddle.Size([1, 3, 16, 16])

            >>> # for return_mask=True
            >>> max_pool2d = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, return_mask=True)
            >>> output, max_indices = max_pool2d(input)
            >>> print(output.shape)
            paddle.Size([1, 3, 16, 16])
            >>> print(max_indices.shape)
            paddle.Size([1, 3, 16, 16])

            >>> # for dilation (dilated max pooling)
            >>> max_pool2d = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, dilation=2)
            >>> output = max_pool2d(input)
            >>> print(output.shape)
            paddle.Size([1, 3, 15, 15])
    """

    kernel_size: Size2
    stride: Size2 | None
    padding: _PaddingSizeMode | Size2 | Size4
    return_mask: bool
    ceil_mode: bool
    dilation: Size2
    data_format: DataLayout2D
    name: str | None

    @maxpool_layer_decorator
    def __init__(
        self,
        kernel_size: Size2,
        stride: Size2 | None = None,
        padding: _PaddingSizeMode | Size2 | Size4 = 0,
        return_mask: bool = False,
        ceil_mode: bool = False,
        dilation: Size2 = 1,
        data_format: DataLayout2D = 'NCHW',
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.ksize = kernel_size
        self.stride = stride
        self.padding = padding
        self.return_mask = return_mask
        self.ceil_mode = ceil_mode
        self.dilation = dilation
        self.data_format = data_format
        self.name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.max_pool2d(
            x=x,
            kernel_size=self.ksize,
            stride=self.stride,
            padding=self.padding,
            return_mask=self.return_mask,
            ceil_mode=self.ceil_mode,
            dilation=self.dilation,
            data_format=self.data_format,
            name=self.name,
        )

    def extra_repr(self) -> str:
        return 'kernel_size={ksize}, stride={stride}, padding={padding}, dilation={dilation}'.format(
            **self.__dict__
        )


class MaxPool3D(Layer):
    """
    This operation applies 3D max pooling over input features based on the input,
    and kernel_size, stride, padding parameters. Input(X) and Output(Out) are
    in NCDHW format, where N is batch size, C is the number of channels,
    H is the height of the feature,  D is the depth of the feature, and W is the width of the feature.

    Parameters:
        kernel_size(int|list|tuple): The pool kernel size. If the kernel size
            is a tuple or list, it must contain three integers,
            (kernel_size_Depth, kernel_size_Height, kernel_size_Width).
            Otherwise, the pool kernel size will be the cube of an int.
        stride(int|list|tuple|None, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain three integers, [stride_Depth, stride_Height, stride_Width).
            Otherwise, the pool stride size will be a cube of an int.
            Default None, then stride will be equal to the kernel_size.
        padding(str|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
            1. A string in ['valid', 'same'].
            2. An int, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 3, [pad_depth, pad_height, pad_weight] whose value means the padding size of each dimension.
            4. A list[int] or tuple(int) whose length is \6. [pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side.
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
            The default value is 0.
        return_mask(bool, optional): Whether to return the max indices along with the outputs.
        ceil_mode(bool, optional): ${ceil_mode_comment}
        dilation(int|list|tuple, optional): The dilation size. If dilation is a tuple or list, it must
            contain three integers, (dilation_Depth, dilation_Height, dilation_Width). Otherwise, the dilation size
            will be a cube of an int. Default 1.
        data_format(str, optional): The data format of the input and output data. An optional string from: `"NCDHW"`,
            `"NDHWC"`. The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_depth, input_height, input_width]`.
        name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.


    Returns:
        A callable object of MaxPool3D.

    Shape:
        - x(Tensor): The input tensor of max pool3d operator, which is a 5-D tensor.
          The data type can be float32, float64.
        - output(Tensor): The output tensor of max pool3d  operator, which is a 5-D tensor.
          The data type is same as input x.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> # max pool3d
            >>> input = paddle.uniform([1, 2, 3, 32, 32], dtype="float32", min=-1, max=1)
            >>> max_pool3d = nn.MaxPool3D(kernel_size=2, stride=2, padding=0)
            >>> output = max_pool3d(input)
            >>> print(output.shape)
            paddle.Size([1, 2, 1, 16, 16])

            >>> # for return_mask=True
            >>> max_pool3d = nn.MaxPool3D(kernel_size=2, stride=2, padding=0, return_mask=True)
            >>> output, max_indices = max_pool3d(input)
            >>> print(output.shape)
            paddle.Size([1, 2, 1, 16, 16])
            >>> print(max_indices.shape)
            paddle.Size([1, 2, 1, 16, 16])

            >>> # for dilation (dilated max pooling)
            >>> max_pool3d = nn.MaxPool3D(kernel_size=2, stride=2, padding=0, dilation=2)
            >>> output = max_pool3d(input)
            >>> print(output.shape)
            paddle.Size([1, 2, 1, 15, 15])
    """

    kernel_size: Size3
    stride: Size3 | None
    padding: _PaddingSizeMode | Size3 | Size6
    return_mask: bool
    ceil_mode: bool
    dilation: Size3
    data_format: DataLayout3D
    name: str | None

    @maxpool_layer_decorator
    def __init__(
        self,
        kernel_size: Size3,
        stride: Size3 | None = None,
        padding: _PaddingSizeMode | Size3 | Size6 = 0,
        return_mask: bool = False,
        ceil_mode: bool = False,
        dilation: Size3 = 1,
        data_format: DataLayout3D = 'NCDHW',
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.ksize = kernel_size
        self.stride = stride
        self.padding = padding
        self.return_mask = return_mask
        self.ceil_mode = ceil_mode
        self.dilation = dilation
        self.data_format = data_format
        self.name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.max_pool3d(
            x=x,
            kernel_size=self.ksize,
            stride=self.stride,
            padding=self.padding,
            return_mask=self.return_mask,
            ceil_mode=self.ceil_mode,
            dilation=self.dilation,
            data_format=self.data_format,
            name=self.name,
        )

    def extra_repr(self) -> str:
        return 'kernel_size={ksize}, stride={stride}, padding={padding}, dilation={dilation}'.format(
            **self.__dict__
        )


class AdaptiveAvgPool1D(Layer):
    r"""

    A 1D adaptive average pooling over an input signal composed
    of several input planes, based on :attr:`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 :math:`[N, C, output\_size]`.

    The formulation for average adaptive pool1d is

    ..  math::

        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}.

    Parameters:
        output_size(int): The target output size. Its data type must be int.
        name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.

    Returns:
        A callable object for computing 1D adaptive average pooling.

    Examples:
        .. code-block:: pycon

            >>> # 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)
            >>> adaptive_avg_pool1d = nn.AdaptiveAvgPool1D(output_size=16)
            >>> pool_out = adaptive_avg_pool1d(data)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 16])
    """

    output_size: int
    name: str | None

    def __init__(self, output_size: int, name: str | None = None) -> None:
        super().__init__()
        self.output_size = output_size
        self.name = name

    def forward(self, input: Tensor) -> Tensor:
        return F.adaptive_avg_pool1d(input, self.output_size, self.name)

    def extra_repr(self) -> str:
        return f'output_size={self.output_size}'


class AdaptiveAvgPool2D(Layer):
    r"""

    This operation applies 2D adaptive avg pooling on input tensor. The h and w dimensions
    of the output tensor are determined by the parameter output_size.

    For avg adaptive pool2d:

    ..  math::

        hstart &= floor(i * H_{in} / H_{out})

        hend &= ceil((i + 1) * H_{in} / H_{out})

        wstart &= floor(j * W_{in} / W_{out})

        wend &= ceil((j + 1) * W_{in} / W_{out})

        Output(i ,j) &= \frac{\sum Input[hstart:hend, wstart:wend]}{(hend - hstart) * (wend - wstart)}


    Parameters:
        output_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain two element, (H, W). H and W can be either a int, or None which means
            the size will be the same as that of the input.
        data_format(str, optional): The data format of the input and output data. An optional string
            from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in
            the order of: [batch_size, input_channels, input_height, input_width].
        name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.

    Shape:
        - x(Tensor): The input tensor of adaptive avg pool2d operator, which is a 4-D tensor.
          The data type can be float32, float64.
        - output(Tensor): The output tensor of adaptive avg pool2d operator, which is a 4-D tensor.
          The data type is same as input x.

    Returns:
        A callable object of AdaptiveAvgPool2D.

    Examples:
        .. code-block:: pycon

            >>> # adaptive avg pool2d
            >>> # suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
            >>> # output shape is [N, C, m, n], adaptive pool divide H and W dimensions
            >>> # of input data into m * n grids averagely and performs poolings in each
            >>> # grid to get output.
            >>> # adaptive avg pool performs calculations as follow:
            >>> #
            >>> #     for i in range(m):
            >>> #         for j in range(n):
            >>> #             hstart = floor(i * H / m)
            >>> #             hend = ceil((i + 1) * H / m)
            >>> #             wstart = floor(i * W / n)
            >>> #             wend = ceil((i + 1) * W / n)
            >>> #             output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
            >>> #
            >>> import paddle

            >>> x = paddle.rand([2, 3, 32, 32])

            >>> adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=3)
            >>> pool_out = adaptive_avg_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 3, 3])
    """

    def __init__(
        self,
        output_size: Size2,
        data_format: DataLayout2D = 'NCHW',
        name: str | None = None,
    ) -> None:
        super().__init__()
        self._output_size = output_size
        self._data_format = data_format
        self._name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.adaptive_avg_pool2d(
            x,
            output_size=self._output_size,
            data_format=self._data_format,
            name=self._name,
        )

    def extra_repr(self) -> str:
        return f'output_size={self._output_size}'

    @property
    def output_size(self) -> Size2:
        return self._output_size

    @output_size.setter
    def output_size(self, value: Size2) -> None:
        self._output_size = value


class AdaptiveAvgPool3D(Layer):
    r"""

    This operation applies 3D adaptive avg pooling on input tensor. The h and w dimensions
    of the output tensor are determined by the parameter output_size.

    For avg adaptive pool3d:

    ..  math::

        dstart &= floor(i * D_{in} / D_{out})

        dend &= ceil((i + 1) * D_{in} / D_{out})

        hstart &= floor(j * H_{in} / H_{out})

        hend &= ceil((j + 1) * H_{in} / H_{out})

        wstart &= floor(k * W_{in} / W_{out})

        wend &= ceil((k + 1) * W_{in} / W_{out})

        Output(i ,j, k) &= \frac{\sum Input[dstart:dend, hstart:hend, wstart:wend]}
            {(dend - dstart) * (hend - hstart) * (wend - wstart)}


    Parameters:
        output_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means
            the size will be the same as that of the input.
        data_format(str, optional): The data format of the input and output data. An optional string
            from: "NCDHW", "NDHWC". The default is "NCDHW". When it is "NCDHW", the data is stored in
            the order of: [batch_size, input_channels, input_depth, input_height, input_width].
        name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.
    Shape:
        - x(Tensor): The input tensor of adaptive avg pool3d operator, which is a 5-D tensor.
          The data type can be float32, float64\.
        - output(Tensor): The output tensor of adaptive avg pool3d operator, which is a 5-D tensor.
          The data type is same as input x.

    Returns:
        A callable object of AdaptiveAvgPool3D.

    Examples:
        .. code-block:: pycon

            >>> # adaptive avg pool3d
            >>> # suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
            >>> # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
            >>> # of input data into l * m * n grids averagely and performs poolings in each
            >>> # grid to get output.
            >>> # adaptive avg pool performs calculations as follow:
            >>> #
            >>> #     for i in range(l):
            >>> #         for j in range(m):
            >>> #             for k in range(n):
            >>> #                 dstart = floor(i * D / l)
            >>> #                 dend = ceil((i + 1) * D / l)
            >>> #                 hstart = floor(j * H / m)
            >>> #                 hend = ceil((j + 1) * H / m)
            >>> #                 wstart = floor(k * W / n)
            >>> #                 wend = ceil((k + 1) * W / n)
            >>> #                 output[:, :, i, j, k] =
            >>> #                     avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
            >>> import paddle

            >>> x = paddle.rand([2, 3, 8, 32, 32])

            >>> adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(output_size=3)
            >>> pool_out = adaptive_avg_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 3, 3, 3])
    """

    def __init__(
        self,
        output_size: Size3,
        data_format: DataLayout3D = 'NCDHW',
        name: str | None = None,
    ) -> None:
        super().__init__()
        self._output_size = output_size
        self._data_format = data_format
        self._name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.adaptive_avg_pool3d(
            x,
            output_size=self._output_size,
            data_format=self._data_format,
            name=self._name,
        )

    def extra_repr(self) -> str:
        return f'output_size={self._output_size}'

    @property
    def output_size(self) -> Size3:
        return self._output_size

    @output_size.setter
    def output_size(self, value: Size3) -> None:
        self._output_size = value


class AdaptiveMaxPool1D(Layer):
    """

    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:

    ..  math::

        lstart &= floor(i * L_{in} / L_{out})

        lend &= ceil((i + 1) * L_{in} / L_{out})

        Output(i) &= max(Input[lstart:lend])

    Parameters:
        output_size(int|list|tuple): The pool kernel size. It can be an integer, or a list or tuple containing a single integer.
        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|None, optional): For detailed information, please refer to :ref:`api_guide_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:
        .. code-block:: pycon

            >>> # 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)
            >>> adaptive_max_pool1d = nn.AdaptiveMaxPool1D(output_size=16)
            >>> pool_out = adaptive_max_pool1d(data)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 16])

            >>> # for return_mask = true
            >>> adaptive_max_pool1d = nn.AdaptiveMaxPool1D(output_size=16, return_mask=True)
            >>> pool_out, indices = adaptive_max_pool1d(data)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 16])
            >>> print(indices.shape)
            paddle.Size([1, 3, 16])

    """

    output_size: Size1
    return_mask: bool
    name: str | None

    @param_one_alias(["return_mask", "return_indices"])
    def __init__(
        self,
        output_size: Size1,
        return_mask: bool = False,
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.output_size = output_size
        self.return_mask = return_mask
        self.name = name

    def forward(self, input: Tensor) -> Tensor:
        return F.adaptive_max_pool1d(
            input, self.output_size, self.return_mask, self.name
        )

    def extra_repr(self) -> str:
        return f'output_size={self.output_size}, return_mask={self.return_mask}'

    @property
    def return_indices(self) -> bool:
        return self.return_mask

    @return_indices.setter
    def return_indices(self, value: bool) -> None:
        self.return_mask = value


class AdaptiveMaxPool2D(Layer):
    """
    This operation applies 2D adaptive max pooling on input tensor. The h and w dimensions
    of the output tensor are determined by the parameter output_size. The difference between adaptive pooling and
    pooling is adaptive one focus on the output size.

    For adaptive max pool2d:

    ..  math::

        hstart &= floor(i * H_{in} / H_{out})

        hend &= ceil((i + 1) * H_{in} / H_{out})

        wstart &= floor(j * W_{in} / W_{out})

        wend &= ceil((j + 1) * W_{in} / W_{out})

        Output(i ,j) &= max(Input[hstart:hend, wstart:wend])

    Parameters:
        output_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain
            two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of
            the input.
        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|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.
    Shape:
        - x(Tensor): The input tensor of adaptive max pool2d operator, which is a 4-D tensor.
          The data type can be float32, float64.
        - output(Tensor): The output tensor of adaptive max pool2d operator, which is a 4-D tensor.
          The data type is same as input x.

    Returns:
        A callable object of AdaptiveMaxPool2D.
    Examples:
        .. code-block:: pycon

            >>> # adaptive max pool2d
            >>> # suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
            >>> # output shape is [N, C, m, n], adaptive pool divide H and W dimensions
            >>> # of input data into m * n grids averagely and performs poolings in each
            >>> # grid to get output.
            >>> # adaptive max pool performs calculations as follow:
            >>> #
            >>> #     for i in range(m):
            >>> #         for j in range(n):
            >>> #             hstart = floor(i * H / m)
            >>> #             hend = ceil((i + 1) * H / m)
            >>> #             wstart = floor(i * W / n)
            >>> #             wend = ceil((i + 1) * W / n)
            >>> #             output[:, :, i, j] = max(input[:, :, hstart: hend, wstart: wend])
            >>> #
            >>> import paddle

            >>> x = paddle.rand([2, 3, 32, 32])

            >>> adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=3, return_mask=True)
            >>> pool_out, indices = adaptive_max_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 3, 3])
            >>> print(indices.shape)
            paddle.Size([2, 3, 3, 3])
    """

    @param_one_alias(["return_mask", "return_indices"])
    def __init__(
        self,
        output_size: Size2,
        return_mask: bool = False,
        name: str | None = None,
    ) -> None:
        super().__init__()
        self._output_size = output_size
        self._return_mask = return_mask
        self._name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.adaptive_max_pool2d(
            x,
            output_size=self._output_size,
            return_mask=self._return_mask,
            name=self._name,
        )

    def extra_repr(self) -> str:
        return (
            f'output_size={self._output_size}, return_mask={self._return_mask}'
        )

    @property
    def return_indices(self) -> bool:
        return self._return_mask

    @return_indices.setter
    def return_indices(self, value: bool) -> None:
        self._return_mask = value


class AdaptiveMaxPool3D(Layer):
    """
    This operation applies 3D adaptive max pooling on input tensor. The h and w dimensions of the output tensor are
    determined by the parameter output_size. The difference between adaptive pooling and pooling is adaptive one focus
    on the output size.

    For adaptive max pool3d:

    ..  math::

        dstart &= floor(i * D_{in} / D_{out})

        dend &= ceil((i + 1) * D_{in} / D_{out})

        hstart &= floor(j * H_{in} / H_{out})

        hend &= ceil((j + 1) * H_{in} / H_{out})

        wstart &= floor(k * W_{in} / W_{out})

        wend &= ceil((k + 1) * W_{in} / W_{out})

        Output(i ,j, k) &= max(Input[dstart:dend, hstart:hend, wstart:wend])

    Parameters:
        output_size(int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain
            three elements, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as
            that of the input.
        return_mask(bool, optional): If true, the index of max pooling point will be returned along with outputs.
            Default False.
        name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.
    Shape:
        - x(Tensor): The input tensor of adaptive max pool3d operator, which is a 5-D tensor.
          The data type can be float32, float64.
        - output(Tensor): The output tensor of adaptive max pool3d operator, which is a 5-D tensor.
          The data type is same as input x.

    Returns:
        A callable object of AdaptiveMaxPool3D.
    Examples:
        .. code-block:: pycon

            >>> # adaptive max pool3d
            >>> # suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
            >>> # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
            >>> # of input data into l * m * n grids averagely and performs poolings in each
            >>> # grid to get output.
            >>> # adaptive max pool performs calculations as follow:
            >>> #
            >>> #     for i in range(l):
            >>> #         for j in range(m):
            >>> #             for k in range(n):
            >>> #                 dstart = floor(i * D / l)
            >>> #                 dend = ceil((i + 1) * D / l)
            >>> #                 hstart = floor(j * H / m)
            >>> #                 hend = ceil((j + 1) * H / m)
            >>> #                 wstart = floor(k * W / n)
            >>> #                 wend = ceil((k + 1) * W / n)
            >>> #                 output[:, :, i, j, k] =
            >>> #                     max(input[:, :, dstart:dend, hstart: hend, wstart: wend])
            >>> import paddle

            >>> x = paddle.rand([2, 3, 8, 32, 32])
            >>> pool = paddle.nn.AdaptiveMaxPool3D(output_size=4)
            >>> out = pool(x)
            >>> print(out.shape)
            paddle.Size([2, 3, 4, 4, 4])
            >>> pool = paddle.nn.AdaptiveMaxPool3D(output_size=3, return_mask=True)
            >>> out, indices = pool(x)
            >>> print(out.shape)
            paddle.Size([2, 3, 3, 3, 3])
            >>> print(indices.shape)
            paddle.Size([2, 3, 3, 3, 3])

    """

    @param_one_alias(["return_mask", "return_indices"])
    def __init__(
        self,
        output_size: Size3,
        return_mask: bool = False,
        name: str | None = None,
    ) -> None:
        super().__init__()
        self._output_size = output_size
        self._return_mask = return_mask
        self._name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.adaptive_max_pool3d(
            x,
            output_size=self._output_size,
            return_mask=self._return_mask,
            name=self._name,
        )

    def extra_repr(self) -> str:
        return (
            f'output_size={self._output_size}, return_mask={self._return_mask}'
        )

    @property
    def return_indices(self) -> bool:
        return self._return_mask

    @return_indices.setter
    def return_indices(self, value: bool) -> None:
        self._return_mask = value


class MaxUnPool1D(Layer):
    r"""
    This API implements max unpooling 1d operation.

    `max_unpool1d` accepts the output of `max_pool1d` as input,
    including the indices of the maximum value and calculate the partial inverse.
    All non-maximum values are set to zero.

    - Input: :math:`(N, C, L_{in})`
    - Output: :math:`(N, C, L_{out})`, where

    .. math::
        L_{out} = (L_{in} - 1) * stride - 2 * padding + kernel\_size

    or as given by :attr:`output_size` in the call operator.

    Parameters:
        kernel_size (int|list|tuple): The unpool kernel size. If unpool kernel size is a tuple or list,
            it must contain an integer.
        stride (int|list|tuple): The unpool stride size. If unpool stride size is a tuple or list,
            it must contain an integer.
        padding (int | tuple): Padding that was added to the input.
        output_size(list|tuple, optional): The target output size. If output_size is not specified,
                           the actual output shape will be automatically calculated by (input_shape,
                           kernel_size, stride, padding).
        data_format (string): The data format of the input and output data.
                        The default is `"NCL"`. When it is `"NCL"`, the data is stored in the order of:
                        `[batch_size, input_channels, input_length]`.
        name(str|None, optional): For detailed information, please refer
                             to :ref:`api_guide_Name`. Usually name is no need to set and
                             None by default.


    Returns:
        A callable object of MaxUnPool1D.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn.functional as F

            >>> data = paddle.rand(shape=[1, 3, 16])
            >>> pool_out, indices = F.max_pool1d(data, kernel_size=2, stride=2, padding=0, return_mask=True)
            >>> print(pool_out.shape)
            paddle.Size([1, 3, 8])
            >>> print(indices.shape)
            paddle.Size([1, 3, 8])
            >>> unpool1d = paddle.nn.MaxUnPool1D(kernel_size=2, padding=0)
            >>> unpool_out = unpool1d(pool_out, indices)
            >>> print(unpool_out.shape)
            paddle.Size([1, 3, 16])

    """

    kernel_size: Size1
    stride: Size1 | None
    padding: _PaddingSizeMode | Size1 | Size2
    data_format: DataLayout1D
    output_size: Sequence[int] | None
    name: str | None

    def __init__(
        self,
        kernel_size: Size1,
        stride: Size1 | None = None,
        padding: _PaddingSizeMode | Size1 | Size2 = 0,
        data_format: DataLayout1D = 'NCL',
        output_size: Sequence[int] | None = None,
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.ksize = kernel_size
        self.stride = stride
        self.padding = padding
        self.data_format = data_format
        self.output_size = output_size
        self.name = name

    @param_one_alias(["x", "input"])
    def forward(
        self,
        x: Tensor,
        indices: Tensor,
        output_size: Sequence[int] | None = None,
    ) -> Tensor:
        if output_size:
            warnings.warn(
                "output_size in forward overrides output_size in __init__. "
                "The output_size parameter in forward has higher priority.",
                stacklevel=2,
            )
            valid_output_size = output_size
        else:
            valid_output_size = self.output_size
        return F.max_unpool1d(
            x,
            indices,
            kernel_size=self.ksize,
            stride=self.stride,
            padding=self.padding,
            data_format=self.data_format,
            output_size=valid_output_size,
            name=self.name,
        )

    def extra_repr(self) -> str:
        return f'output_size={self.output_size}'


class MaxUnPool2D(Layer):
    r"""
    This API implements max unpooling 2d operation.

    'max_unpool2d' accepts the output of 'max_unpool2d' as input
    Including the indices of the maximum value and calculating the partial inverse
    All non-maximum values are set to zero.


    Parameters:
        kernel_size (int|list|tuple): The unpool kernel size. If unpool kernel size is a tuple or list,
            it must contain an integer.
        stride (int|list|tuple): The unpool stride size. If unpool stride size is a tuple or list,
            it must contain an integer.
        kernel_size (int|tuple): Size of the max unpooling window.
        padding (int|tuple): Padding that was added to the input.
        output_size(list|tuple, optional): The target output size. If output_size is not specified,
                           the actual output shape will be automatically calculated by (input_shape,
                           kernel_size, padding).
        data_format (string): The data format of the input and output data.
                        The default is `"NCL"`. When it is `"NCL"`, the data is stored in the order of:
                        `[batch_size, input_channels, input_length]`.
        name(str|None, optional): For detailed information, please refer
                             to :ref:`api_guide_Name`. Usually name is no need to set and
                             None by default.


        - Input: :math:`(N, C, H_{in}, W_{in})`
        - Output: :math:`(N, C, H_{out}, W_{out})`, where

          .. math::
            H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}

          .. math::
            W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}

          or as given by :attr:`output_size` in the call operator

    Returns:
        A callable object of MaxUnPool2D.



    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn.functional as F

            >>> data = paddle.rand(shape=[1, 1, 6, 6])
            >>> pool_out, indices = F.max_pool2d(data, kernel_size=2, stride=2, padding=0, return_mask=True)
            >>> print(pool_out.shape)
            paddle.Size([1, 1, 3, 3])
            >>> print(indices.shape)
            paddle.Size([1, 1, 3, 3])
            >>> unpool2d = paddle.nn.MaxUnPool2D(kernel_size=2, padding=0)
            >>> unpool_out = unpool2d(pool_out, indices)
            >>> print(unpool_out.shape)
            paddle.Size([1, 1, 6, 6])

    """

    kernel_size: Size2
    stride: Size2 | None
    padding: _PaddingSizeMode | Size2 | Size4
    data_format: DataLayout2D
    output_size: Sequence[int] | None
    name: str | None

    def __init__(
        self,
        kernel_size: Size2,
        stride: Size2 | None = None,
        padding: _PaddingSizeMode | Size2 | Size4 = 0,
        data_format: DataLayout2D = 'NCHW',
        output_size: Sequence[int] | None = None,
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.ksize = kernel_size
        self.stride = stride
        self.padding = padding
        self.data_format = data_format
        self.output_size = output_size
        self.name = name

    @param_one_alias(["x", "input"])
    def forward(
        self,
        x: Tensor,
        indices: Tensor,
        output_size: Sequence[int] | None = None,
    ) -> Tensor:
        if output_size:
            warnings.warn(
                "output_size in forward overrides output_size in __init__. "
                "The output_size parameter in forward has higher priority.",
                stacklevel=2,
            )
            valid_output_size = output_size
        else:
            valid_output_size = self.output_size

        return F.max_unpool2d(
            x,
            indices,
            kernel_size=self.ksize,
            stride=self.stride,
            padding=self.padding,
            data_format=self.data_format,
            output_size=valid_output_size,
            name=self.name,
        )

    def extra_repr(self) -> str:
        return f'output_size={self.output_size}'


class MaxUnPool3D(Layer):
    r"""
    This API implements max unpooling 3d operation.

    `max_unpool3d` accepts the output of `max_pool3d` as input,
    including the indices of the maximum value and calculate the partial inverse.
    All non-maximum values are set to zero.

    - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})`
    - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})`, where

    .. math::
        D_{out} = (D_{in} - 1) * stride[0] - 2 * padding[0] + kernel\_size[0]

    .. math::
        H_{out} = (H_{in} - 1) * stride[1] - 2 * padding[1] + kernel\_size[1]

    .. math::
        W_{out} = (W_{in} - 1) * stride[2] - 2 * padding[2] + kernel\_size[2]

    or as given by :attr:`output_size` in the call operator


    Parameters:
        kernel_size (int|list|tuple): The unpool kernel size. If unpool kernel size is a tuple or list,
            it must contain an integer.
        stride (int|list|tuple|None): The unpool stride size. If unpool stride size is a tuple or list,
            it must contain an integer.
        padding (int | tuple): Padding that was added to the input.
        output_size(list|tuple, optional): The target output size. If output_size is not specified,
                           the actual output shape will be automatically calculated by (input_shape,
                           kernel_size, stride, padding).
        data_format (string): The data format of the input and output data.
                        The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
                        `[batch_size, input_channels, input_depth, input_height, input_width]`.
        name(str|None, optional): For detailed information, please refer
                             to :ref:`api_guide_Name`. Usually name is no need to set and
                             None by default.


    Returns:
        A callable object of MaxUnPool3D.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn.functional as F

            >>> data = paddle.rand(shape=[1, 1, 4, 4, 6])
            >>> pool_out, indices = F.max_pool3d(data, kernel_size=2, stride=2, padding=0, return_mask=True)
            >>> print(pool_out.shape)
            paddle.Size([1, 1, 2, 2, 3])
            >>> print(indices.shape)
            paddle.Size([1, 1, 2, 2, 3])
            >>> unpool3d = paddle.nn.MaxUnPool3D(kernel_size=2, padding=0)
            >>> unpool_out = unpool3d(pool_out, indices)
            >>> print(unpool_out.shape)
            paddle.Size([1, 1, 4, 4, 6])

    """

    kernel_size: Size3
    stride: Size3 | None
    padding: _PaddingSizeMode | Size3 | Size6
    data_format: DataLayout3D
    output_size: Sequence[int] | None
    name: str | None

    def __init__(
        self,
        kernel_size: Size3,
        stride: Size3 | None = None,
        padding: _PaddingSizeMode | Size3 | Size6 = 0,
        data_format: DataLayout3D = 'NCDHW',
        output_size: Sequence[int] | None = None,
        name: str | None = None,
    ) -> None:
        super().__init__()
        self.ksize = kernel_size
        self.stride = stride
        self.padding = padding
        self.data_format = data_format
        self.output_size = output_size
        self.name = name

    @param_one_alias(["x", "input"])
    def forward(
        self,
        x: Tensor,
        indices: Tensor,
        output_size: Sequence[int] | None = None,
    ) -> Tensor:
        if output_size:
            warnings.warn(
                "output_size in forward overrides output_size in __init__. "
                "The output_size parameter in forward has higher priority.",
                stacklevel=2,
            )
            valid_output_size = output_size
        else:
            valid_output_size = self.output_size
        return F.max_unpool3d(
            x,
            indices,
            kernel_size=self.ksize,
            stride=self.stride,
            padding=self.padding,
            data_format=self.data_format,
            output_size=valid_output_size,
            name=self.name,
        )

    def extra_repr(self) -> str:
        return f'output_size={self.output_size}'


class FractionalMaxPool2D(Layer):
    r"""
    This operation applies 2D fractional max pooling on input tensor, which is described in the paper:

    [1] Ben Graham, Fractional Max-Pooling. 2015. http://arxiv.org/abs/1412.6071

    The h and w dimensions of the output tensor are determined by the parameter output_size.

    For each dimension, the fractional max pooling:

    .. math::

        \alpha &= size_{input} / size_{output}

        index_{start} &= ceil( \alpha * (i + u) - 1)

        index_{end} &= ceil( \alpha * (i + 1 + u) - 1)

        Output &= max(Input[index_{start}:index_{end}])

        where, u \in (0, 1), i = 0,1,2...size_{output}

    The ``u`` from the formula is the parameter ``random_u``, and subtract ``1`` for the index starts from ``0``
    instead of ``1`` where ``ceil`` works.

    For instance, giving a sequence of length ``7`` is ``[2, 4, 3, 1, 5, 2, 3]``, ``output_size`` is ``5`` and ``random_u`` is ``0.3``.
    The ``alpha = 7/5 = 1.4``, the starts of index is ``[0, 1, 3, 4, 6]``, the ends of index is ``[1, 3, 4, 6, 7]`` and makes the
    random sequence in the paper is ``index_end - index_start = [1, 2, 1, 2, 1]``. The strides and kernel_sizes are both equal to
    the random sequence, giving the final pooling output is ``[2, 4, 1, 5, 3]``.

    Parameters:
        output_size(int|list|tuple): The output size. If output size is a tuple or list, it must contain
            two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of
            the input.
        kernel_size (int|list|tuple|None, optional): The pool kernel size. If the kernel size
            is a tuple or list, it must contain two integers, (kernel_size_Height, kernel_size_Width).
            Otherwise, the pool kernel size will be the square of an int. Default is None, means using the non-overlapping mode.
        random_u(float|None, optional): A random float number in range (0, 1) for the fractional pooling.
            Default None, means randomly generated by framework which can be fixed by ``paddle.seed``.
        return_mask(bool, optional): If true, the index of max pooling point will be returned along with outputs. Default False.
        name(str|None, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.

    Shape:
        - x(Tensor): The input tensor of fractional max pool2d operator, which is a 4-D tensor.
          The data type can be float16, bfloat16, float32, float64.
        - output(Tensor): The output tensor of fractional max pool2d operator, which is a 4-D tensor.
          The data type is same as input x.

    Returns:
        A callable object of FractionalMaxPool2D.

    Examples:
        .. code-block:: pycon

            >>> # fractional max pool2d
            >>> # suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
            >>> # output shape is [N, C, m, n], fractional pool divide H and W dimensions
            >>> # of input data into m * n grids and performs poolings in each
            >>> # grid to get output.

            >>> import paddle

            >>> x = paddle.rand([2, 3, 32, 32])

            >>> # disjoint: without `kernel_size`
            >>> fractional_max_pool = paddle.nn.FractionalMaxPool2D(output_size=3)
            >>> pool_out = fractional_max_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 3, 3])

            >>> # overlapping: with `kernel_size`
            >>> fractional_max_pool = paddle.nn.FractionalMaxPool2D(kernel_size=2, output_size=3)
            >>> pool_out = fractional_max_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 3, 3])

            >>> fractional_max_pool = paddle.nn.FractionalMaxPool2D(output_size=[2, 3], return_mask=True)
            >>> pool_out, indices = fractional_max_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 2, 3])
            >>> print(indices.shape)
            paddle.Size([2, 3, 2, 3])
    """

    @param_one_alias(["return_mask", "return_indices"])
    def __init__(
        self,
        output_size: Size2,
        kernel_size: Size2 | None = None,
        random_u: float | None = None,
        return_mask: bool = False,
        name: str | None = None,
    ):
        super().__init__()
        self._output_size = output_size
        self._kernel_size = kernel_size
        self._random_u = random_u
        self._return_mask = return_mask
        self._name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.fractional_max_pool2d(
            x,
            output_size=self._output_size,
            kernel_size=self._kernel_size,
            random_u=self._random_u,
            return_mask=self._return_mask,
            name=self._name,
        )

    def extra_repr(self) -> Tensor:
        return (
            f'output_size={self._output_size}, return_mask={self._return_mask}'
        )


class FractionalMaxPool3D(Layer):
    r"""
    This operation applies 3D fractional max pooling on input tensor, which is described in the paper:

    [1] Ben Graham, Fractional Max-Pooling. 2015. http://arxiv.org/abs/1412.6071

    The d, h and w dimensions of the output tensor are determined by the parameter output_size.

    For each dimension, the fractional max pooling:

    .. math::

        \alpha &= size_{input} / size_{output}

        index_{start} &= ceil( \alpha * (i + u) - 1)

        index_{end} &= ceil( \alpha * (i + 1 + u) - 1)

        Output &= max(Input[index_{start}:index_{end}])

        where, u \in (0, 1), i = 0,1,2...size_{output}

    The ``u`` from the formula is the parameter ``random_u``, and subtract ``1`` for the index starts from ``0``
    instead of ``1`` where ``ceil`` works.

    For instance, giving a sequence of length ``7`` is ``[2, 4, 3, 1, 5, 2, 3]``, ``output_size`` is ``5`` and ``random_u`` is ``0.3``.
    The ``alpha = 7/5 = 1.4``, the starts of index is ``[0, 1, 3, 4, 6]``, the ends of index is ``[1, 3, 4, 6, 7]`` and makes the
    random sequence in the paper is ``index_end - index_start = [1, 2, 1, 2, 1]``. The strides and kernel_sizes are both equal to
    the random sequence, giving the final pooling output is ``[2, 4, 1, 5, 3]``.

    Parameters:
        output_size(int|list|tuple): The output size. If output size is a tuple or list, it must contain
            three element, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as that of
            the input.
        kernel_size (int|list|tuple|None): The pool kernel size. If the kernel size
            is a tuple or list, it must contain three integers, (kernel_size_Depth, kernel_size_Height, kernel_size_Width).
            Otherwise, the pool kernel size will be the cube of an int. Default is None, means using the non-overlapping mode.
        random_u(float|None, optional): A random float number in range (0, 1) for the fractional pooling.
            Default None, means randomly generated by framework which can be fixed by ``paddle.seed``.
        return_mask(bool, optional): If true, the index of max pooling point will be returned along with outputs. Default False.
        name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`.
            Usually name is no need to set and None by default.

    Shape:
        - x(Tensor): The input tensor of fractional max pool3d operator, which is a 5-D tensor.
          The data type can be float16, bfloat16, float32, float64.
        - output(Tensor): The output tensor of fractional max pool3d operator, which is a 5-D tensor.
          The data type is same as input x.

    Returns:
        A callable object of FractionalMaxPool3D.

    Examples:
        .. code-block:: pycon

            >>> # fractional max pool3d
            >>> # suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
            >>> # output shape is [N, C, l, m, n], fractional pool divide D, H and W dimensions
            >>> # of input data into l * m * n grids and performs poolings in each
            >>> # grid to get output.

            >>> import paddle

            >>> x = paddle.rand([2, 3, 8, 32, 32])

            >>> # disjoint: without `kernel_size`
            >>> fractional_max_pool = paddle.nn.FractionalMaxPool3D(output_size=3)
            >>> pool_out = fractional_max_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 3, 3, 3])

            >>> # overlapping: with `kernel_size`
            >>> fractional_max_pool = paddle.nn.FractionalMaxPool3D(kernel_size=2, output_size=3)
            >>> pool_out = fractional_max_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 3, 3, 3])

            >>> fractional_max_pool = paddle.nn.FractionalMaxPool3D(output_size=[2, 3, 3], return_mask=True)
            >>> pool_out, indices = fractional_max_pool(x=x)
            >>> print(pool_out.shape)
            paddle.Size([2, 3, 2, 3, 3])
            >>> print(indices.shape)
            paddle.Size([2, 3, 2, 3, 3])
    """

    @param_one_alias(["return_mask", "return_indices"])
    def __init__(
        self,
        output_size: Size2,
        kernel_size: Size2 | None = None,
        random_u: float | None = None,
        return_mask: bool = False,
        name: str | None = None,
    ):
        super().__init__()
        self._output_size = output_size
        self._kernel_size = kernel_size
        self._random_u = random_u
        self._return_mask = return_mask
        self._name = name

    @param_one_alias(["x", "input"])
    def forward(self, x: Tensor) -> Tensor:
        return F.fractional_max_pool3d(
            x,
            output_size=self._output_size,
            kernel_size=self._kernel_size,
            random_u=self._random_u,
            return_mask=self._return_mask,
            name=self._name,
        )

    def extra_repr(self) -> str:
        return (
            f'output_size={self._output_size}, return_mask={self._return_mask}'
        )