Pytorch上下采样函数--interpolate-创新互联

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小编给大家分享一下Pytorch上下采样函数--interpolate，希望大家阅读完这篇文章后大所收获，下面让我们一起去探讨吧！

最近用到了上采样下采样操作，pytorch中使用interpolate可以很轻松的完成

def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None):
  r"""
  根据给定 size 或 scale_factor，上采样或下采样输入数据input.
  
  当前支持 temporal, spatial 和 volumetric 输入数据的上采样，其shape 分别为：3-D, 4-D 和 5-D.
  输入数据的形式为：mini-batch x channels x [optional depth] x [optional height] x width.

  上采样算法有：nearest, linear(3D-only), bilinear(4D-only), trilinear(5D-only).
  
  参数:
  - input (Tensor): input tensor
  - size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):输出的 spatial 尺寸.
  - scale_factor (float or Tuple[float]): spatial 尺寸的缩放因子.
  - mode (string): 上采样算法:nearest, linear, bilinear, trilinear, area. 默认为 nearest.
  - align_corners (bool, optional): 如果 align_corners=True，则对齐 input 和 output 的角点像素(corner pixels)，保持在角点像素的值. 只会对 mode=linear, bilinear 和 trilinear 有作用. 默认是 False.
  """
  from numbers import Integral
  from .modules.utils import _ntuple

  def _check_size_scale_factor(dim):
    if size is None and scale_factor is None:
      raise ValueError('either size or scale_factor should be defined')
    if size is not None and scale_factor is not None:
      raise ValueError('only one of size or scale_factor should be defined')
    if scale_factor is not None and isinstance(scale_factor, tuple)\
        and len(scale_factor) != dim:
      raise ValueError('scale_factor shape must match input shape. '
               'Input is {}D, scale_factor size is {}'.format(dim, len(scale_factor)))

  def _output_size(dim):
    _check_size_scale_factor(dim)
    if size is not None:
      return size
    scale_factors = _ntuple(dim)(scale_factor)
    # math.floor might return float in py2.7
    return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)]

  if mode in ('nearest', 'area'):
    if align_corners is not None:
      raise ValueError("align_corners option can only be set with the "
               "interpolating modes: linear | bilinear | trilinear")
  else:
    if align_corners is None:
      warnings.warn("Default upsampling behavior when mode={} is changed "
             "to align_corners=False since 0.4.0. Please specify "
             "align_corners=True if the old behavior is desired. "
             "See the documentation of nn.Upsample for details.".format(mode))
      align_corners = False

  if input.dim() == 3 and mode == 'nearest':
    return torch._C._nn.upsample_nearest1d(input, _output_size(1))
  elif input.dim() == 4 and mode == 'nearest':
    return torch._C._nn.upsample_nearest2d(input, _output_size(2))
  elif input.dim() == 5 and mode == 'nearest':
    return torch._C._nn.upsample_nearest3d(input, _output_size(3))
  elif input.dim() == 3 and mode == 'area':
    return adaptive_avg_pool1d(input, _output_size(1))
  elif input.dim() == 4 and mode == 'area':
    return adaptive_avg_pool2d(input, _output_size(2))
  elif input.dim() == 5 and mode == 'area':
    return adaptive_avg_pool3d(input, _output_size(3))
  elif input.dim() == 3 and mode == 'linear':
    return torch._C._nn.upsample_linear1d(input, _output_size(1), align_corners)
  elif input.dim() == 3 and mode == 'bilinear':
    raise NotImplementedError("Got 3D input, but bilinear mode needs 4D input")
  elif input.dim() == 3 and mode == 'trilinear':
    raise NotImplementedError("Got 3D input, but trilinear mode needs 5D input")
  elif input.dim() == 4 and mode == 'linear':
    raise NotImplementedError("Got 4D input, but linear mode needs 3D input")
  elif input.dim() == 4 and mode == 'bilinear':
    return torch._C._nn.upsample_bilinear2d(input, _output_size(2), align_corners)
  elif input.dim() == 4 and mode == 'trilinear':
    raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input")
  elif input.dim() == 5 and mode == 'linear':
    raise NotImplementedError("Got 5D input, but linear mode needs 3D input")
  elif input.dim() == 5 and mode == 'bilinear':
    raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input")
  elif input.dim() == 5 and mode == 'trilinear':
    return torch._C._nn.upsample_trilinear3d(input, _output_size(3), align_corners)
  else:
    raise NotImplementedError("Input Error: Only 3D, 4D and 5D input Tensors supported"
                 " (got {}D) for the modes: nearest | linear | bilinear | trilinear"
                 " (got {})".format(input.dim(), mode))