MegEngine/imperative/python/megengine/module/pooling.py

# -*- coding: utf-8 -*-
from abc import abstractmethod
from typing import Tuple, Union

from ..functional import avg_pool2d, max_pool2d
from .module import Module


class _PoolNd(Module):
    def __init__(
        self,
        kernel_size: Union[int, Tuple[int, int]],
        stride: Union[int, Tuple[int, int]] = None,
        padding: Union[int, Tuple[int, int]] = 0,
        **kwargs
    ):
        super(_PoolNd, self).__init__(**kwargs)
        self.kernel_size = kernel_size
        self.stride = stride or kernel_size
        self.padding = padding

    @abstractmethod
    def forward(self, inp):
        pass

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


class MaxPool2d(_PoolNd):
    r"""Applies a 2D max pooling over an input.

    For instance, given an input of the size :`(N, C, H_{\text{in}}, W_{\text{in}})` and
    :attr:`kernel_size` :math:`(kH, kW)`, this layer generates the output of
    the size :math:`(N, C, H_{\text{out}}, W_{\text{out}})` through a process described as:

    .. math::

        \begin{aligned}
            out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1}
                \text{input}(N_i, C_j, \text{stride[0]} \times h + m,
                \text{stride[1]} \times w + n)
        \end{aligned}

    If :attr:`padding` is non-zero, then the input is implicitly zero-padded on
    both sides for :attr:`padding` number of points.

    Args:
        kernel_size: the size of the window.
        stride: the stride of the window. Default value is ``kernel_size``.
        padding: implicit zero padding to be added on both sides.Default: 0.

    Examples:
        >>> import numpy as np
        >>> m = M.MaxPool2d(kernel_size=3, stride=1, padding=0)
        >>> inp = mge.tensor(np.arange(0, 16).astype("float32").reshape(1, 1, 4, 4))
        >>> oup = m(inp)
        >>> oup.numpy()
        array([[[[10., 11.],
                 [14., 15.]]]], dtype=float32)
    """

    def forward(self, inp):
        return max_pool2d(inp, self.kernel_size, self.stride, self.padding)


class AvgPool2d(_PoolNd):
    r"""Applies a 2D average pooling over an input.

    For instance, given an input of the size :math:`(N, C, H_{\text{in}}, W_{\text{in}})` and
    :attr:`kernel_size` :math:`(kH, kW)`, this layer generates the output of
    the size :math:`(N, C, H_{\text{out}}, W_{\text{out}})` through a process described as:

    .. math::

        out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
                               input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)

    If :attr:`padding` is non-zero, then the input is implicitly zero-padded on
    both sides for :attr:`padding` number of points.

    Args:
        kernel_size: the size of the window.
        stride: the stride of the window. Default value is ``kernel_size``.
        padding: implicit zero padding to be added on both sides.Default: 0.
        mode: whether to include the padding values while calculating the average, set
            to "average" will do counting.
            Default: "average_count_exclude_padding"

    Examples:
        >>> import numpy as np
        >>> m = M.AvgPool2d(kernel_size=2, stride=2, padding=[1,0], mode="average")
        >>> inp = mge.tensor(np.arange(1 * 1 * 3 * 4).astype(np.float32).reshape(1, 1, 3, 4))
        >>> output = m(inp)
        >>> output
        Tensor([[[[0.25 1.25]
                  [6.5  8.5 ]]]], device=xpux:0)

    """

    def __init__(
        self,
        kernel_size: Union[int, Tuple[int, int]],
        stride: Union[int, Tuple[int, int]] = None,
        padding: Union[int, Tuple[int, int]] = 0,
        mode: str = "average_count_exclude_padding",
        **kwargs
    ):
        super(AvgPool2d, self).__init__(kernel_size, stride, padding, **kwargs)
        self.mode = mode

    def forward(self, inp):
        return avg_pool2d(inp, self.kernel_size, self.stride, self.padding, self.mode)

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