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local SpatialDownSampling, parent = torch.class('nn.SpatialDownSampling', 'nn.Module')

local help_desc = [[

Applies a 2D down-sampling over an input image composed of

several input planes. The input tensor in forward(input) is

expected to be a 3D tensor (nInputPlane x width x height).

The number of output planes will be the same as nInputPlane.

The downsampling is done using the simple average

technique. For interpolated (bicubic) downsampling, use

nn.SpatialReSampling().

If the input image is a 3D tensor nInputPlane x width x height,

the output image size will be nInputPlane x owidth x oheight where

owidth = floor(width/rW)

oheight = floor(height/rH) ]]

function SpatialDownSampling:__init(...)

parent.__init(self)

-- get args

xlua.unpack_class(self, {...}, 'nn.SpatialDownSampling', help_desc,

{arg='rW', type='number', help='ratio width', req=true},

{arg='rH', type='number', help='ratio height', req=true})

end

function SpatialDownSampling:updateOutput(input)

self.output:resize(input:size(1), math.floor(input:size(2) / self.rH),

math.floor(input:size(3) / self.rW))

input.nn.SpatialDownSampling_updateOutput(self, input)

return self.output

end

function SpatialDownSampling:updateGradInput(input, gradOutput)

self.gradInput:resizeAs(input)

input.nn.SpatialDownSampling_updateGradInput(self, gradOutput)

return self.gradInput

end