# -*- 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 :`(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(Union[int, Tuple[int, int]]): the size of the window.
stride(Union[int, Tuple[int, int]]): the stride of the window. Default value is ``kernel_size``.
padding(Union[int, Tuple[int, int]]): implicit zero padding to be added on both sides.Default: 0.
Shape:
- Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`
- Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
.. math::
H_{out} = \left\lfloor\frac{H_{in} + 2 * \text{padding[0]} - \text{dilation[0]}
\times (\text{kernel\_size[0]} - 1) - 1}{\text{stride[0]}} + 1\right\rfloor
.. math::
W_{out} = \left\lfloor\frac{W_{in} + 2 * \text{padding[1]} - \text{dilation[1]}
\times (\text{kernel\_size[1]} - 1) - 1}{\text{stride[1]}} + 1\right\rfloor
Returns:
Return type: module. The instance of the ``MaxPool2d`` module.
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_{\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(Union[int, Tuple[int, int]]): the size of the window.
stride(Union[int, Tuple[int, int]]): the stride of the window. Default value is ``kernel_size``.
padding(Union[int, Tuple[int, int]]): implicit zero padding to be added on both sides.Default: 0.
mode(str): whether to include the padding values while calculating the average, set
to "average" will do counting.
Default: "average_count_exclude_padding"
Shape:
- Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
- Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
.. math::
H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] -
\text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor
.. math::
W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] -
\text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor
Returns:
Return type: module. The instance of the ``AvgPool2d`` module.
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__
)