megengine.module.adaptive_pooling 源代码

# -*- 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


[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) 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)
[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) 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)