# -*- coding: utf-8 -*-
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
[docs]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)
Args:
oshp(Union[Tuple[int, int], int, Tensor]): the target output shape of the image of the form Height * Width.
Can be tuple (H, W) or a single H for a square image H * H.
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}, W_{out})=\text{output\_shape}`.
Returns:
Return type: module. The instance of the ``AdaptiveMaxPool2d`` module.
Examples:
>>> import numpy as np
>>> m = M.AdaptiveMaxPool2d((2, 2))
>>> inp = mge.tensor(np.arange(0, 16).astype("float32").reshape(1, 1, 4, 4))
>>> oup = m(inp)
>>> oup.numpy()
array([[[[ 5., 7.],
[13., 15.]]]], dtype=float32)
"""
def forward(self, inp):
return adaptive_max_pool2d(inp, self.oshp)
[docs]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)
Args:
oshp(Union[Tuple[int, int], int, Tensor]): the target output shape of the image of the form Height * Width.
Can be tuple (H, W) or a single H for a square image H * H.
Shape:
- Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`.
- Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or :math:`(C, D_{out}, H_{out}, W_{out})`,
where :math:`(D_{out}, H_{out}, W_{out})=\text{output\_shape}`.
Returns:
Return type: module. The instance of the ``AdaptiveAvgPool2d`` module.
Examples:
>>> import numpy as np
>>> m = M.AdaptiveAvgPool2d((2, 2))
>>> inp = mge.tensor(np.arange(0, 16).astype("float32").reshape(1, 1, 4, 4))
>>> oup = m(inp)
>>> oup.numpy()
array([[[[ 2.5, 4.5],
[10.5, 12.5]]]], dtype=float32)
"""
def forward(self, inp):
return adaptive_avg_pool2d(inp, self.oshp)