pytorch中的上采样以及各种反操作，求逆操作

import torch.nn.functional as F

import torch.nn as nn 

 F.upsample(input, size=None, scale_factor=None,mode='nearest', align_corners=None)

    r"""Upsamples the input to either the given :attr:`size` or the given
    :attr:`scale_factor`

    The algorithm used for upsampling is determined by :attr:`mode`.

    Currently temporal, spatial and volumetric upsampling are supported, i.e.
    expected inputs are 3-D, 4-D or 5-D in shape.

    The input dimensions are interpreted in the form:
    `mini-batch x channels x [optional depth] x [optional height] x width`.

    The modes available for upsampling are: `nearest`, `linear` (3D-only),
    `bilinear` (4D-only), `trilinear` (5D-only)

    Args:
        input (Tensor): the input tensor
        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
            output spatial size.
        scale_factor (int): multiplier for spatial size. Has to be an integer.
        mode (string): algorithm used for upsampling:
            'nearest' | 'linear' | 'bilinear' | 'trilinear'. Default: 'nearest'
        align_corners (bool, optional): if True, the corner pixels of the input
            and output tensors are aligned, and thus preserving the values at
            those pixels. This only has effect when :attr:`mode` is `linear`,
            `bilinear`, or `trilinear`. Default: False

    .. warning::
        With ``align_corners = True``, the linearly interpolating modes
        (`linear`, `bilinear`, and `trilinear`) don't proportionally align the
        output and input pixels, and thus the output values can depend on the
        input size. This was the default behavior for these modes up to version
        0.3.1. Since then, the default behavior is ``align_corners = False``.
        See :class:`~torch.nn.Upsample` for concrete examples on how this
        affects the outputs.

    """

nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1)

"""
Parameters:	
    in_channels (int) – Number of channels in the input image
    out_channels (int) – Number of channels produced by the convolution
    kernel_size (int or tuple) – Size of the convolving kernel
    stride (int or tuple, optional) – Stride of the convolution. Default: 1
    padding (int or tuple, optional) – kernel_size - 1 - padding zero-padding will be added to both sides of each dimension in the input. Default: 0
    output_padding (int or tuple, optional) – Additional size added to one side of each dimension in the output shape. Default: 0
    groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
    bias (bool, optional) – If True, adds a learnable bias to the output. Default: True
    dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1


"""

计算方式：

定义：nn.MaxUnpool2d(kernel_size, stride=None, padding=0)

调用：

def forward(self, input, indices, output_size=None):
    return F.max_unpool2d(input, indices, self.kernel_size, self.stride,
                          self.padding, output_size)

    r"""Computes a partial inverse of :class:`MaxPool2d`.

    :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.

    :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
    including the indices of the maximal values and computes a partial inverse
    in which all non-maximal values are set to zero.

    .. note:: `MaxPool2d` can map several input sizes to the same output sizes.
              Hence, the inversion process can get ambiguous.
              To accommodate this, you can provide the needed output size
              as an additional argument `output_size` in the forward call.
              See the Inputs and Example below.

    Args:
        kernel_size (int or tuple): Size of the max pooling window.
        stride (int or tuple): Stride of the max pooling window.
            It is set to ``kernel_size`` by default.
        padding (int or tuple): Padding that was added to the input

    Inputs:
        - `input`: the input Tensor to invert
        - `indices`: the indices given out by `MaxPool2d`
        - `output_size` (optional) : a `torch.Size` that specifies the targeted output size

    Shape:
        - Input: :math:`(N, C, H_{in}, W_{in})`
        - Output: :math:`(N, C, H_{out}, W_{out})` where

    计算公式：见下面

    Example: 见下面


    """

F. max_unpool2d(input, indices, kernel_size, stride=None, padding=0, output_size=None)

见上面的用法一致！

def max_unpool2d(input, indices, kernel_size, stride=None, padding=0,
                 output_size=None):
    r"""Computes a partial inverse of :class:`MaxPool2d`.

    See :class:`~torch.nn.MaxUnpool2d` for details.
    """
    pass