megengine.module.adaptive_pooling 源代码

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 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],
    ):
        super(_AdaptivePoolNd, self).__init__()
        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)