megengine.module.pooling 源代码

# -*- 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__
        )


[文档]class MaxPool2d(_PoolNd): r"""Applies a 2D max pooling over an input. For instance, given an input of the size :math:`(N, C, H, W)` and :attr:`kernel_size` :math:`(kH, kW)`, this layer generates the output of the size :math:`(N, C, H_{out}, W_{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 to take a max over. stride: the stride of the window. Default value is kernel_size. padding: implicit zero padding to be added on both sides. 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)
[文档]class AvgPool2d(_PoolNd): r"""Applies a 2D average pooling over an input. For instance, given an input of the size :math:`(N, C, H, W)` and :attr:`kernel_size` :math:`(kH, kW)`, this layer generates the output of the size :math:`(N, C, H_{out}, W_{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. mode: whether to count padding values. "average" mode will do counting and "average_count_exclude_padding" mode won't do counting. Default: "average_count_exclude_padding" """ 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__ )