# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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:
.. testcode::
import numpy as np
import megengine as mge
import megengine.module as M
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)
print(oup.numpy())
Outputs:
.. testoutput::
[[[[10. 11.]
[14. 15.]]]]
"""
[文档] 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
[文档] 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__
)