[docs]classUpsample(Module):r"""Upsamples 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]]]]) """__constants__=['size','scale_factor','mode','align_corners','name']name:strsize:Optional[_size_any_t]scale_factor:Optional[_ratio_any_t]mode:stralign_corners:Optional[bool]def__init__(self,size:Optional[_size_any_t]=None,scale_factor:Optional[_ratio_any_t]=None,mode:str='nearest',align_corners:Optional[bool]=None)->None:super(Upsample,self).__init__()self.name=type(self).__name__self.size=sizeifisinstance(scale_factor,tuple):self.scale_factor=tuple(float(factor)forfactorinscale_factor)else:self.scale_factor=float(scale_factor)ifscale_factorelseNoneself.mode=modeself.align_corners=align_cornersdefforward(self,input:Tensor)->Tensor:returnF.interpolate(input,self.size,self.scale_factor,self.mode,self.align_corners)defextra_repr(self)->str:ifself.scale_factorisnotNone:info='scale_factor='+str(self.scale_factor)else:info='size='+str(self.size)info+=', mode='+self.modereturninfo
[docs]classUpsamplingNearest2d(Upsample):r"""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.]]]]) """def__init__(self,size:Optional[_size_2_t]=None,scale_factor:Optional[_ratio_2_t]=None)->None:super(UpsamplingNearest2d,self).__init__(size,scale_factor,mode='nearest')
[docs]classUpsamplingBilinear2d(Upsample):r"""Applies 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]]]]) """def__init__(self,size:Optional[_size_2_t]=None,scale_factor:Optional[_ratio_2_t]=None)->None:super(UpsamplingBilinear2d,self).__init__(size,scale_factor,mode='bilinear',align_corners=True)
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