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Source code for torch.nn.modules.upsampling

from .module import Module
from .. import functional as F

from torch import Tensor
from typing import Optional
from ..common_types import _size_2_t, _ratio_2_t, _size_any_t, _ratio_any_t

__all__ = ['Upsample', 'UpsamplingNearest2d', 'UpsamplingBilinear2d']


[docs]class Upsample(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'``, ``'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]]]]) """ __constants__ = ['size', 'scale_factor', 'mode', 'align_corners', 'name', 'recompute_scale_factor'] name: str size: Optional[_size_any_t] scale_factor: Optional[_ratio_any_t] mode: str align_corners: Optional[bool] recompute_scale_factor: 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, recompute_scale_factor: Optional[bool] = None) -> None: super().__init__() self.name = type(self).__name__ self.size = size if isinstance(scale_factor, tuple): self.scale_factor = tuple(float(factor) for factor in scale_factor) else: self.scale_factor = float(scale_factor) if scale_factor else None self.mode = mode self.align_corners = align_corners self.recompute_scale_factor = recompute_scale_factor def forward(self, input: Tensor) -> Tensor: return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners, recompute_scale_factor=self.recompute_scale_factor) def __setstate__(self, state): if 'recompute_scale_factor' not in state: state['recompute_scale_factor'] = True super().__setstate__(state) def extra_repr(self) -> str: if self.scale_factor is not None: info = 'scale_factor=' + repr(self.scale_factor) else: info = 'size=' + repr(self.size) info += ', mode=' + repr(self.mode) return info
[docs]class UpsamplingNearest2d(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().__init__(size, scale_factor, mode='nearest')
[docs]class UpsamplingBilinear2d(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.]]]]) >>> # 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]]]]) """ def __init__(self, size: Optional[_size_2_t] = None, scale_factor: Optional[_ratio_2_t] = None) -> None: super().__init__(size, scale_factor, mode='bilinear', align_corners=True)

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