/usr/local/lib64/python3.6/site-packages/torch/nn/modules/__pycache__
NameSizeModeActions
activation.cpython-36.pyc476350644editdlrm
adaptive.cpython-36.pyc97250644editdlrm
batchnorm.cpython-36.pyc310390644editdlrm
channelshuffle.cpython-36.pyc19010644editdlrm
container.cpython-36.pyc279700644editdlrm
conv.cpython-36.pyc577890644editdlrm
distance.cpython-36.pyc36840644editdlrm
dropout.cpython-36.pyc103100644editdlrm
flatten.cpython-36.pyc58130644editdlrm
fold.cpython-36.pyc128070644editdlrm
instancenorm.cpython-36.pyc188080644editdlrm
lazy.cpython-36.pyc116900644editdlrm
linear.cpython-36.pyc101880644editdlrm
loss.cpython-36.pyc914650644editdlrm
module.cpython-36.pyc665300644editdlrm
normalization.cpython-36.pyc113600644editdlrm
padding.cpython-36.pyc222170644editdlrm
pixelshuffle.cpython-36.pyc43980644editdlrm
pooling.cpython-36.pyc532030644editdlrm
rnn.cpython-36.pyc446530644editdlrm
sparse.cpython-36.pyc209560644editdlrm
transformer.cpython-36.pyc207370644editdlrm
upsampling.cpython-36.pyc107800644editdlrm
utils.cpython-36.pyc25280644editdlrm
_functions.cpython-36.pyc54410644editdlrm
__init__.cpython-36.pyc52360644editdlrm
Edit: /usr/local/lib64/python3.6/site-packages/torch/nn/modules/__pycache__/upsampling.cpython-36.pyc (10780B)
3 Egd'@s|ddlmZddlmZddlmZddlmZddl m Z m Z m Z m Z Gdd d eZGd d d eZGd d d eZdS))Module) functional)Tensor)Optional) _size_2_t _ratio_2_t _size_any_t _ratio_any_tcseZdZUdZdddddgZeee ee  e ee deeee eee dd fd d Zeed d dZedddZZS)UpsampleaUpsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data. The input data is assumed to be of the form `minibatch x channels x [optional depth] x [optional height] x width`. Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor. The algorithms available for upsampling are nearest neighbor and linear, bilinear, bicubic and trilinear for 3D, 4D and 5D input Tensor, respectively. One can either give a :attr:`scale_factor` or the target output :attr:`size` to calculate the output size. (You cannot give both, as it is ambiguous) Args: size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int], optional): output spatial sizes scale_factor (float or Tuple[float] or Tuple[float, float] or Tuple[float, float, float], optional): multiplier for spatial size. Has to match input size if it is a tuple. mode (str, optional): the upsampling algorithm: one of ``'nearest'``, ``'linear'``, ``'bilinear'``, ``'bicubic'`` and ``'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`` Shape: - Input: :math:`(N, C, W_{in})`, :math:`(N, C, H_{in}, W_{in})` or :math:`(N, C, D_{in}, H_{in}, W_{in})` - Output: :math:`(N, C, W_{out})`, :math:`(N, C, H_{out}, W_{out})` or :math:`(N, C, D_{out}, H_{out}, W_{out})`, where .. math:: D_{out} = \left\lfloor D_{in} \times \text{scale\_factor} \right\rfloor .. math:: H_{out} = \left\lfloor H_{in} \times \text{scale\_factor} \right\rfloor .. math:: W_{out} = \left\lfloor W_{in} \times \text{scale\_factor} \right\rfloor .. warning:: With ``align_corners = True``, the linearly interpolating modes (`linear`, `bilinear`, `bicubic`, 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 below for concrete examples on how this affects the outputs. .. note:: If you want downsampling/general resizing, you should use :func:`~nn.functional.interpolate`. Examples:: >>> input = torch.arange(1, 5, dtype=torch.float32).view(1, 1, 2, 2) >>> input tensor([[[[ 1., 2.], [ 3., 4.]]]]) >>> m = nn.Upsample(scale_factor=2, mode='nearest') >>> m(input) tensor([[[[ 1., 1., 2., 2.], [ 1., 1., 2., 2.], [ 3., 3., 4., 4.], [ 3., 3., 4., 4.]]]]) >>> m = nn.Upsample(scale_factor=2, mode='bilinear') # align_corners=False >>> m(input) tensor([[[[ 1.0000, 1.2500, 1.7500, 2.0000], [ 1.5000, 1.7500, 2.2500, 2.5000], [ 2.5000, 2.7500, 3.2500, 3.5000], [ 3.0000, 3.2500, 3.7500, 4.0000]]]]) >>> m = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) >>> m(input) tensor([[[[ 1.0000, 1.3333, 1.6667, 2.0000], [ 1.6667, 2.0000, 2.3333, 2.6667], [ 2.3333, 2.6667, 3.0000, 3.3333], [ 3.0000, 3.3333, 3.6667, 4.0000]]]]) >>> # Try scaling the same data in a larger tensor >>> >>> input_3x3 = torch.zeros(3, 3).view(1, 1, 3, 3) >>> input_3x3[:, :, :2, :2].copy_(input) tensor([[[[ 1., 2.], [ 3., 4.]]]]) >>> input_3x3 tensor([[[[ 1., 2., 0.], [ 3., 4., 0.], [ 0., 0., 0.]]]]) >>> m = nn.Upsample(scale_factor=2, mode='bilinear') # align_corners=False >>> # Notice that values in top left corner are the same with the small input (except at boundary) >>> m(input_3x3) tensor([[[[ 1.0000, 1.2500, 1.7500, 1.5000, 0.5000, 0.0000], [ 1.5000, 1.7500, 2.2500, 1.8750, 0.6250, 0.0000], [ 2.5000, 2.7500, 3.2500, 2.6250, 0.8750, 0.0000], [ 2.2500, 2.4375, 2.8125, 2.2500, 0.7500, 0.0000], [ 0.7500, 0.8125, 0.9375, 0.7500, 0.2500, 0.0000], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]]) >>> m = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) >>> # Notice that values in top left corner are now changed >>> m(input_3x3) tensor([[[[ 1.0000, 1.4000, 1.8000, 1.6000, 0.8000, 0.0000], [ 1.8000, 2.2000, 2.6000, 2.2400, 1.1200, 0.0000], [ 2.6000, 3.0000, 3.4000, 2.8800, 1.4400, 0.0000], [ 2.4000, 2.7200, 3.0400, 2.5600, 1.2800, 0.0000], [ 1.2000, 1.3600, 1.5200, 1.2800, 0.6400, 0.0000], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]]) size scale_factormode align_cornersnameNnearest)r rrrreturncsbtt|jt|j|_||_t|tr@tdd|D|_ n|rLt |nd|_ ||_ ||_ dS)Ncss|]}t|VqdS)N)float).0ZfactorrG/usr/local/lib64/python3.6/site-packages/torch/nn/modules/upsampling.py sz$Upsample.__init__..) superr __init__type__name__rr isinstancetuplerrrr)selfr rrr) __class__rrrs  zUpsample.__init__)inputrcCstj||j|j|j|jS)N)FZ interpolater rrr)rr!rrrforwardszUpsample.forward)rcCs:|jdk rdt|j}ndt|j}|d|j7}|S)Nz scale_factor=zsize=z, mode=)rstrr r)rinforrr extra_reprs  zUpsample.extra_repr)NNrN)r __module__ __qualname____doc__Z __constants__r$rrr r r rrboolrrrr#r& __classcell__rr)r rr s o( r cs4eZdZdZdeeeeddfdd ZZS)UpsamplingNearest2da"Applies a 2D nearest neighbor upsampling to an input signal composed of several input channels. To specify the scale, it takes either the :attr:`size` or the :attr:`scale_factor` as it's constructor argument. When :attr:`size` is given, it is the output size of the image `(h, w)`. Args: size (int or Tuple[int, int], optional): output spatial sizes scale_factor (float or Tuple[float, float], optional): multiplier for spatial size. .. warning:: This class is deprecated in favor of :func:`~nn.functional.interpolate`. Shape: - Input: :math:`(N, C, H_{in}, W_{in})` - Output: :math:`(N, C, H_{out}, W_{out})` where .. math:: H_{out} = \left\lfloor H_{in} \times \text{scale\_factor} \right\rfloor .. math:: W_{out} = \left\lfloor W_{in} \times \text{scale\_factor} \right\rfloor Examples:: >>> input = torch.arange(1, 5, dtype=torch.float32).view(1, 1, 2, 2) >>> input tensor([[[[ 1., 2.], [ 3., 4.]]]]) >>> m = nn.UpsamplingNearest2d(scale_factor=2) >>> m(input) tensor([[[[ 1., 1., 2., 2.], [ 1., 1., 2., 2.], [ 3., 3., 4., 4.], [ 3., 3., 4., 4.]]]]) N)r rrcstt|j||dddS)Nr)r)rr,r)rr r)r rrrszUpsamplingNearest2d.__init__)NN) rr'r(r)rrr rr+rr)r rr,s(r,cs4eZdZdZdeeeeddfdd ZZS)UpsamplingBilinear2daApplies a 2D bilinear upsampling to an input signal composed of several input channels. To specify the scale, it takes either the :attr:`size` or the :attr:`scale_factor` as it's constructor argument. When :attr:`size` is given, it is the output size of the image `(h, w)`. Args: size (int or Tuple[int, int], optional): output spatial sizes scale_factor (float or Tuple[float, float], optional): multiplier for spatial size. .. warning:: This class is deprecated in favor of :func:`~nn.functional.interpolate`. It is equivalent to ``nn.functional.interpolate(..., mode='bilinear', align_corners=True)``. Shape: - Input: :math:`(N, C, H_{in}, W_{in})` - Output: :math:`(N, C, H_{out}, W_{out})` where .. math:: H_{out} = \left\lfloor H_{in} \times \text{scale\_factor} \right\rfloor .. math:: W_{out} = \left\lfloor W_{in} \times \text{scale\_factor} \right\rfloor Examples:: >>> input = torch.arange(1, 5, dtype=torch.float32).view(1, 1, 2, 2) >>> input tensor([[[[ 1., 2.], [ 3., 4.]]]]) >>> m = nn.UpsamplingBilinear2d(scale_factor=2) >>> m(input) tensor([[[[ 1.0000, 1.3333, 1.6667, 2.0000], [ 1.6667, 2.0000, 2.3333, 2.6667], [ 2.3333, 2.6667, 3.0000, 3.3333], [ 3.0000, 3.3333, 3.6667, 4.0000]]]]) N)r rrcstt|j||ddddS)NZbilinearT)rr)rr-r)rr r)r rrrszUpsamplingBilinear2d.__init__)NN) rr'r(r)rrr rr+rr)r rr-s)r-N)modulerrr"Ztorchrtypingr common_typesrr r r r r,r-rrrrs    -