
    sg`                     b    d dl mc mZ d dlmZ ddlmZ ddgZ G d de      Z	 G d de      Z
y)	    N)Tensor   )ModulePixelShufflePixelUnshufflec                   Z     e Zd ZU dZdgZeed<   deddf fdZdedefdZ	de
fdZ xZS )	r   aS  Rearrange elements in a tensor according to an upscaling factor.

    Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)`
    to a tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is an upscale factor.

    This is useful for implementing efficient sub-pixel convolution
    with a stride of :math:`1/r`.

    See the paper:
    `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
    by Shi et al. (2016) for more details.

    Args:
        upscale_factor (int): factor to increase spatial resolution by

    Shape:
        - Input: :math:`(*, C_{in}, H_{in}, W_{in})`, where * is zero or more batch dimensions
        - Output: :math:`(*, C_{out}, H_{out}, W_{out})`, where

    .. math::
        C_{out} = C_{in} \div \text{upscale\_factor}^2

    .. math::
        H_{out} = H_{in} \times \text{upscale\_factor}

    .. math::
        W_{out} = W_{in} \times \text{upscale\_factor}

    Examples::

        >>> pixel_shuffle = nn.PixelShuffle(3)
        >>> input = torch.randn(1, 9, 4, 4)
        >>> output = pixel_shuffle(input)
        >>> print(output.size())
        torch.Size([1, 1, 12, 12])

    .. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
        https://arxiv.org/abs/1609.05158
    upscale_factorreturnNc                 0    t         |           || _        y N)super__init__r	   )selfr	   	__class__s     P/var/www/html/venv/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.pyr   zPixelShuffle.__init__6   s    ,    inputc                 B    t        j                  || j                        S r   )Fpixel_shuffler	   r   r   s     r   forwardzPixelShuffle.forward:   s    ud&9&9::r   c                      d| j                    S )Nzupscale_factor=)r	   r   s    r   
extra_reprzPixelShuffle.extra_repr=   s     !4!4 566r   __name__
__module____qualname____doc____constants__int__annotations__r   r   r   strr   __classcell__r   s   @r   r   r   
   sK    &P &&M-s -t -;V ; ;7C 7r   c                   Z     e Zd ZU dZdgZeed<   deddf fdZdedefdZ	de
fdZ xZS )	r   a  Reverse the PixelShuffle operation.

    Reverses the :class:`~torch.nn.PixelShuffle` operation by rearranging elements
    in a tensor of shape :math:`(*, C, H \times r, W \times r)` to a tensor of shape
    :math:`(*, C \times r^2, H, W)`, where r is a downscale factor.

    See the paper:
    `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
    by Shi et al. (2016) for more details.

    Args:
        downscale_factor (int): factor to decrease spatial resolution by

    Shape:
        - Input: :math:`(*, C_{in}, H_{in}, W_{in})`, where * is zero or more batch dimensions
        - Output: :math:`(*, C_{out}, H_{out}, W_{out})`, where

    .. math::
        C_{out} = C_{in} \times \text{downscale\_factor}^2

    .. math::
        H_{out} = H_{in} \div \text{downscale\_factor}

    .. math::
        W_{out} = W_{in} \div \text{downscale\_factor}

    Examples::

        >>> pixel_unshuffle = nn.PixelUnshuffle(3)
        >>> input = torch.randn(1, 1, 12, 12)
        >>> output = pixel_unshuffle(input)
        >>> print(output.size())
        torch.Size([1, 9, 4, 4])

    .. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
        https://arxiv.org/abs/1609.05158
    downscale_factorr
   Nc                 0    t         |           || _        y r   )r   r   r(   )r   r(   r   s     r   r   zPixelUnshuffle.__init__k   s     0r   r   c                 B    t        j                  || j                        S r   )r   pixel_unshuffler(   r   s     r   r   zPixelUnshuffle.forwardo   s      (=(=>>r   c                      d| j                    S )Nzdownscale_factor=)r(   r   s    r   r   zPixelUnshuffle.extra_reprr   s    "4#8#8"9::r   r   r&   s   @r   r   r   A   sK    $L ((M1 1 1?V ? ?;C ;r   )torch.nn.functionalnn
functionalr   torchr   moduler   __all__r   r    r   r   <module>r4      s7        +
,476 47n2;V 2;r   