# -*- 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 adaptive_avg_pool2d, adaptive_max_pool2d
from ..tensor import Parameter, Tensor
from .module import Module
class _AdaptivePoolNd(Module):
def __init__(self, oshp: Union[Tuple[int, int], int, Tensor], **kwargs):
super(_AdaptivePoolNd, self).__init__(**kwargs)
self.oshp = oshp
@abstractmethod
def forward(self, inp):
pass
[文档]class AdaptiveMaxPool2d(_AdaptivePoolNd):
r"""Applies a 2D max adaptive pooling over an input.
For instance, given an input of the size :math:`(N, C, H, W)` and
an output shape :math:`(OH, OW)`, this layer generates the output of
the size :math:`(N, C, OH, OW)` 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}
``kernel_size`` and ``stride`` can be inferred from input shape and out shape:
* padding: (0, 0)
* stride: (floor(IH / OH), floor(IW / OW))
* kernel_size: (IH - (OH - 1) * stride_h, IW - (OW - 1) * stride_w)
Examples:
.. testcode::
import numpy as np
import megengine as mge
import megengine.module as M
m = M.AdaptiveMaxPool2d((2, 2))
inp = mge.tensor(np.arange(0, 16).astype("float32").reshape(1, 1, 4, 4))
oup = m(inp)
print(oup.numpy())
Outputs:
.. testoutput::
[[[[ 5. 7.]
[13. 15.]]]]
"""
[文档] def forward(self, inp):
return adaptive_max_pool2d(inp, self.oshp)
[文档]class AdaptiveAvgPool2d(_AdaptivePoolNd):
r"""Applies a 2D average pooling over an input.
For instance, given an input of the size :math:`(N, C, H, W)` and
an output shape :math:`(OH, OW)`, this layer generates the output of
the size :math:`(N, C, OH, OW)` 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)
``kernel_size`` and ``stride`` can be inferred from input shape and out shape:
* padding: (0, 0)
* stride: (floor(IH / OH), floor(IW / OW))
* kernel_size: (IH - (OH - 1) * stride_h, IW - (OW - 1) * stride_w)
Examples:
.. testcode::
import numpy as np
import megengine as mge
import megengine.module as M
m = M.AdaptiveAvgPool2d((2, 2))
inp = mge.tensor(np.arange(0, 16).astype("float32").reshape(1, 1, 4, 4))
oup = m(inp)
print(oup.numpy())
Outputs:
.. testoutput::
[[[[ 2.5 4.5]
[10.5 12.5]]]]
"""
[文档] def forward(self, inp):
return adaptive_avg_pool2d(inp, self.oshp)