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local SpatialUpSamplingNearest, parent = torch.class('nn.SpatialUpSamplingNearest', 'nn.Module')
--[[
Applies a 2D up-sampling over an input image composed of several input planes.
The upsampling is done using the simple nearest neighbor technique.
The Y and X dimensions are assumed to be the last 2 tensor dimensions. For
instance, if the tensor is 4D, then dim 3 is the y dimension and dim 4 is the x.
owidth = width*scale_factor
oheight = height*scale_factor
--]]
function SpatialUpSamplingNearest:__init(scale)
parent.__init(self)
self.scale_factor = scale
if self.scale_factor < 1 then
error('scale_factor must be greater than 1')
end
if math.floor(self.scale_factor) ~= self.scale_factor then
error('scale_factor must be integer')
end
self.inputSize = torch.LongStorage(4)
self.outputSize = torch.LongStorage(4)
end
function SpatialUpSamplingNearest:updateOutput(input)
if input:dim() ~= 4 and input:dim() ~= 3 then
error('SpatialUpSamplingNearest only support 3D or 4D tensors')
end
-- Copy the input size
local xdim = input:dim()
local ydim = input:dim() - 1
for i = 1, input:dim() do
self.inputSize[i] = input:size(i)
self.outputSize[i] = input:size(i)
end
self.outputSize[ydim] = self.outputSize[ydim] * self.scale_factor
self.outputSize[xdim] = self.outputSize[xdim] * self.scale_factor
input.THNN.SpatialUpSamplingNearest_updateOutput(
input:cdata(),
self.output:cdata(),
self.scale_factor
)
return self.output
end
function SpatialUpSamplingNearest:updateGradInput(input, gradOutput)
self.gradInput:resizeAs(input)
input.THNN.SpatialUpSamplingNearest_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.scale_factor
)
return self.gradInput
end
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