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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)
if (input:size(2) / self.rH) < 1 then
error('input too small in dimension 2')
elseif (input:size(3) / self.rW) < 1 then
error('input too small in dimension 3')
end
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
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