Source code for torch.nn.modules.pixelshuffle

from .module import Module
from .. import functional as F
from ..._jit_internal import weak_module, weak_script_method


@weak_module
class PixelShuffle(Module):
    r"""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)`.

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

    Look at 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:`(N, C \times \text{upscale_factor}^2, H, W)`
        - Output: :math:`(N, C, H \times \text{upscale_factor}, W \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
    """
    __constants__ = ['upscale_factor']

    def __init__(self, upscale_factor):
        super(PixelShuffle, self).__init__()
        self.upscale_factor = upscale_factor

    @weak_script_method
    def forward(self, input):
        return F.pixel_shuffle(input, self.upscale_factor)

    def extra_repr(self):
        return 'upscale_factor={}'.format(self.upscale_factor)