L i-ddlmZddlmcmZddlmZddlm Z m Z m Z m Z ddl mZgdZGdd eZGd d eZGd d eZy))OptionalN)Tensor) _ratio_2_t _ratio_any_t _size_2_t _size_any_t)Module)UpsampleUpsamplingNearest2dUpsamplingBilinear2dc eZdZUdZgdZeed<eeed<ee ed<eed<ee ed<ee ed< ddeedee dedee dee d d f fd Z d e d e fd Z fdZd efdZxZS)r aUpsamples 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'``, ``'bicubic'``, or ``'trilinear'``. Default: ``False`` recompute_scale_factor (bool, optional): recompute the scale_factor for use in the interpolation calculation. If `recompute_scale_factor` is ``True``, then `scale_factor` must be passed in and `scale_factor` is used to compute the output `size`. The computed output `size` will be used to infer new scales for the interpolation. Note that when `scale_factor` is floating-point, it may differ from the recomputed `scale_factor` due to rounding and precision issues. If `recompute_scale_factor` is ``False``, then `size` or `scale_factor` will be used directly for interpolation. 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.]]]]) >>> # xdoctest: +IGNORE_WANT("other tests seem to modify printing styles") >>> 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.]]]]) >>> # xdoctest: +IGNORE_WANT("seems to fail when other tests are run in the same session") >>> 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_cornersnamerecompute_scale_factorrrrrrrNreturnct|t|j|_||_t |trtd|D|_n|r t|nd|_||_ ||_ ||_ y)Nc32K|]}t|yw)N)float).0factors a/mnt/ssd/data/python-lab/Trading/venv/lib/python3.12/site-packages/torch/nn/modules/upsampling.py z$Upsample.__init__..s%OeFm%Os) super__init__type__name__rr isinstancetuplerrrrr)selfrrrrr __class__s rrzUpsample.__init__sm J''  lE * %%O,%O OD 7Cl 3D  *&<#inputctj||j|j|j|j |j S)z( Runs the forward pass. )r)F interpolaterrrrr)r#r&s rforwardzUpsample.forwardsA}}  II    II   #'#>#>   r%c6d|vrd|d<t||y)NrT)r __setstate__)r#stater$s rr,zUpsample.__setstate__s# #5 0.2E* + U#r%c|jdt|jz}ndt|jz}|dt|jzz }|S)z@ Return the extra representation of the module. z scale_factor=zsize=z, mode=)rreprrr)r#infos r extra_reprzUpsample.extra_reprsS    ("T$*;*;%<!$TN*'+/3(,15 ={#=|,= =  ~ = !) = =&  V    $ C r%r cBeZdZdZ ddeedeeddffd ZxZS)r a 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.]]]]) Nrrrc*t|||dy)Nr2)rrrr#rrr$s rrzUpsamplingNearest2d.__init__s |)>> input = torch.arange(1, 5, dtype=torch.float32).view(1, 1, 2, 2) >>> input tensor([[[[1., 2.], [3., 4.]]]]) >>> # xdoctest: +IGNORE_WANT("do other tests modify the global state?") >>> 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]]]]) Nrrrc,t|||ddy)NbilinearT)rrr>r?s rrzUpsamplingBilinear2d.__init__&s |*DQr%r@rAr;s@rr r sF(X%)-1Ry!Rz*R  RRr%r )typingrtorch.nn.functionalnn functionalr(torchrtorch.nn.common_typesrrrrmoduler __all__r r r r%rrNsLRR Fyvyx.=(.=b0R80Rr%