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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)

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