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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


[docs]@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)

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