Source code for torch.nn.modules.upsampling

from numbers import Integral

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


class _UpsamplingBase(Module):

    def __init__(self, size=None, scale_factor=None):
        super(_UpsamplingBase, self).__init__()
        if size is None and scale_factor is None:
            raise ValueError('either size or scale_factor should be defined')
        if scale_factor is not None and not isinstance(scale_factor, Integral):
            raise ValueError('scale_factor must be of integer type')
        self.size = _pair(size)
        self.scale_factor = scale_factor

    def __repr__(self):
        if self.scale_factor is not None:
            info = 'scale_factor=' + str(self.scale_factor)
        else:
            info = 'size=' + str(self.size)
        return self.__class__.__name__ + '(' + info + ')'


[docs]class UpsamplingNearest2d(_UpsamplingBase): """ 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 `size` is given, it is the output size of the image (h, w). Args: size (tuple, optional): a tuple of ints (H_out, W_out) output sizes scale_factor (int, optional): the multiplier for the image height / width Shape: - Input: :math:`(N, C, H_{in}, W_{in})` - Output: :math:`(N, C, H_{out}, W_{out})` where :math:`H_{out} = floor(H_{in} * scale\_factor)` :math:`W_{out} = floor(W_{in} * scale\_factor)` Examples:: >>> inp Variable containing: (0 ,0 ,.,.) = 1 2 3 4 [torch.FloatTensor of size 1x1x2x2] >>> m = nn.UpsamplingNearest2d(scale_factor=2) >>> m(inp) Variable containing: (0 ,0 ,.,.) = 1 1 2 2 1 1 2 2 3 3 4 4 3 3 4 4 [torch.FloatTensor of size 1x1x4x4] """ def forward(self, input): return F.upsample_nearest(input, self.size, self.scale_factor)
[docs]class UpsamplingBilinear2d(_UpsamplingBase): """ 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 `size` is given, it is the output size of the image (h, w). Args: size (tuple, optional): a tuple of ints (H_out, W_out) output sizes scale_factor (int, optional): the multiplier for the image height / width Shape: - Input: :math:`(N, C, H_{in}, W_{in})` - Output: :math:`(N, C, H_{out}, W_{out})` where :math:`H_{out} = floor(H_{in} * scale\_factor)` :math:`W_{out} = floor(W_{in} * scale\_factor)` Examples:: >>> inp Variable containing: (0 ,0 ,.,.) = 1 2 3 4 [torch.FloatTensor of size 1x1x2x2] >>> m = nn.UpsamplingBilinear2d(scale_factor=2) >>> m(inp) Variable containing: (0 ,0 ,.,.) = 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 [torch.FloatTensor of size 1x1x4x4] """ def forward(self, input): return F.upsample_bilinear(input, self.size, self.scale_factor)