Name	Size	Mode	Actions
activation.cpython-36.pyc	47635	0644	editdlrm
adaptive.cpython-36.pyc	9725	0644	editdlrm
batchnorm.cpython-36.pyc	31039	0644	editdlrm
channelshuffle.cpython-36.pyc	1901	0644	editdlrm
container.cpython-36.pyc	27970	0644	editdlrm
conv.cpython-36.pyc	57789	0644	editdlrm
distance.cpython-36.pyc	3684	0644	editdlrm
dropout.cpython-36.pyc	10310	0644	editdlrm
flatten.cpython-36.pyc	5813	0644	editdlrm
fold.cpython-36.pyc	12807	0644	editdlrm
instancenorm.cpython-36.pyc	18808	0644	editdlrm
lazy.cpython-36.pyc	11690	0644	editdlrm
linear.cpython-36.pyc	10188	0644	editdlrm
loss.cpython-36.pyc	91465	0644	editdlrm
module.cpython-36.pyc	66530	0644	editdlrm
normalization.cpython-36.pyc	11360	0644	editdlrm
padding.cpython-36.pyc	22217	0644	editdlrm
pixelshuffle.cpython-36.pyc	4398	0644	editdlrm
pooling.cpython-36.pyc	53203	0644	editdlrm
rnn.cpython-36.pyc	44653	0644	editdlrm
sparse.cpython-36.pyc	20956	0644	editdlrm
transformer.cpython-36.pyc	20737	0644	editdlrm
upsampling.cpython-36.pyc	10780	0644	editdlrm
utils.cpython-36.pyc	2528	0644	editdlrm
_functions.cpython-36.pyc	5441	0644	editdlrm
__init__.cpython-36.pyc	5236	0644	editdlrm

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dd„Z‡ZS) ÚPixelShufflea 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 Úupscale_factorN)rÚreturncstt
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dd„Z‡ZS) ÚPixelUnshuffleaòReverses 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 Údownscale_factorN)rr cstt
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