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local SpatialReSampling, parent = torch.class('nn.SpatialReSampling', 'nn.Module')
local help_desc =
[[Applies a 2D re-sampling over an input image composed of
several input planes. The input tensor in forward(input) is
expected to be a 3D or 4D tensor ([batchSize x nInputPlane x width x height).
The number of output planes will be the same as the nb of input
planes.
The re-sampling is done using bilinear interpolation. For a
simple nearest-neihbor upsampling, use nn.SpatialUpSampling(),
and for a simple average-based down-sampling, use
nn.SpatialDownSampling().
If the input image is a 3D tensor nInputPlane x height x width,
the output image size will be nInputPlane x oheight x owidth where
owidth and oheight are given to the constructor.
Instead of owidth & oheight, one can provide rwidth & rheight,
such that owidth = iwidth*rwidth & oheight = iheight*rheight. ]]
function SpatialReSampling:__init(...)
parent.__init(self)
xlua.unpack_class(
self, {...}, 'nn.SpatialReSampling', help_desc,
{arg='rwidth', type='number', help='ratio: owidth/iwidth'},
{arg='rheight', type='number', help='ratio: oheight/iheight'},
{arg='owidth', type='number', help='output width'},
{arg='oheight', type='number', help='output height'}
)
end
function SpatialReSampling:updateOutput(input)
assert(input:dim() == 3 or input:dim() == 4,
'input to SpatialReSampling must be 3D or 4D, received: [' ..
table.concat(input:size():totable(), ', ') .. ']')
local hDim, wDim = 2, 3
if input:dim() == 4 then
hDim, wDim = 3, 4
end
self.oheight = self.oheight or self.rheight*input:size(hDim)
self.owidth = self.owidth or self.rwidth*input:size(wDim)
input.nn.SpatialReSampling_updateOutput(self, input)
return self.output
end
function SpatialReSampling:updateGradInput(input, gradOutput)
input.nn.SpatialReSampling_updateGradInput(self, input, gradOutput)
return self.gradInput
end
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