from numbers import Integral
import warnings
from .module import Module
from .. import functional as F
[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 [depth] x [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 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 (tuple, optional): a tuple of ints ([D_out], [H_out], W_out) output sizes
scale_factor (int / tuple of ints, optional): the multiplier for the image height / width / depth
mode (string, optional): the upsampling algorithm: nearest | linear | bilinear | trilinear. Default: nearest
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} = floor(D_{in} * scale\_factor)` or `size[-3]`
:math:`H_{out} = floor(H_{in} * scale\_factor)` or `size[-2]`
:math:`W_{out} = floor(W_{in} * scale\_factor)` or `size[-1]`
Examples::
>>> inp
Variable containing:
(0 ,0 ,.,.) =
1 2
3 4
[torch.FloatTensor of size 1x1x2x2]
>>> m = nn.Upsample(scale_factor=2, mode='bilinear')
>>> 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]
>>> inp
Variable containing:
(0 ,0 ,.,.) =
1 2
3 4
[torch.FloatTensor of size 1x1x2x2]
>>> m = nn.Upsample(scale_factor=2, mode='nearest')
>>> 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 __init__(self, size=None, scale_factor=None, mode='nearest'):
super(Upsample, self).__init__()
self.size = size
self.scale_factor = scale_factor
self.mode = mode
def forward(self, input):
return F.upsample(input, self.size, self.scale_factor, self.mode)
def __repr__(self):
if self.scale_factor is not None:
info = 'scale_factor=' + str(self.scale_factor)
else:
info = 'size=' + str(self.size)
info += ', mode=' + self.mode
return self.__class__.__name__ + '(' + 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 `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 __init__(self, size=None, scale_factor=None):
super(UpsamplingNearest2d, self).__init__(size, scale_factor, mode='nearest')
def forward(self, input):
warnings.warn("nn.UpsamplingNearest2d is deprecated. Use nn.Upsample instead.")
return super(UpsamplingNearest2d, self).forward(input)
[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 `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 __init__(self, size=None, scale_factor=None):
super(UpsamplingBilinear2d, self).__init__(size, scale_factor, mode='bilinear')
def forward(self, input):
warnings.warn("nn.UpsamplingBilinear2d is deprecated. Use nn.Upsample instead.")
return super(UpsamplingBilinear2d, self).forward(input)