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local SpatialUpSampling, parent = torch.class('nn.SpatialUpSampling', 'nn.Module')
local help_desc = [[
Applies a 2D up-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 upsampling is done using the simple nearest neighbor
technique. For interpolated (bicubic) upsampling, 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 = width*dW
oheight = height*dH ]]
function SpatialUpSampling:__init(...)
parent.__init(self)
-- get args
xlua.unpack_class(self, {...}, 'nn.SpatialUpSampling', help_desc,
{arg='dW', type='number', help='stride width', req=true},
{arg='dH', type='number', help='stride height', req=true},
{arg='yDim', type='number', help='image y dimension', default=2},
{arg='xDim', type='number', help='image x dimension', default=3}
)
if self.yDim+1 ~= self.xDim then
error('nn.SpatialUpSampling: yDim must be equals to xDim-1')
end
self.outputSize = torch.LongStorage(4)
self.inputSize = torch.LongStorage(4)
end
function SpatialUpSampling:updateOutput(input)
self.inputSize:fill(1)
for i = 1,self.yDim-1 do
self.inputSize[1] = self.inputSize[1] * input:size(i)
end
self.inputSize[2] = input:size(self.yDim)
self.inputSize[3] = input:size(self.xDim)
for i = self.xDim+1,input:nDimension() do
self.inputSize[4] = self.inputSize[4] * input:size(i)
end
self.outputSize[1] = self.inputSize[1]
self.outputSize[2] = self.inputSize[2] * self.dH
self.outputSize[3] = self.inputSize[3] * self.dW
self.outputSize[4] = self.inputSize[4]
self.output:resize(self.outputSize)
input.nn.SpatialUpSampling_updateOutput(self, input:reshape(self.inputSize))
local outputSize2 = input:size()
outputSize2[self.yDim] = outputSize2[self.yDim] * self.dH
outputSize2[self.xDim] = outputSize2[self.xDim] * self.dW
self.output = self.output:reshape(outputSize2)
return self.output
end
function SpatialUpSampling:updateGradInput(input, gradOutput)
self.gradInput:resize(self.inputSize)
input.nn.SpatialUpSampling_updateGradInput(self, input,
gradOutput:reshape(self.outputSize))
self.gradInput = self.gradInput:reshape(input:size())
return self.gradInput
end
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