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local SpatialMaxUnpooling, parent = torch.class('nn.SpatialMaxUnpooling', 'nn.Module')
function SpatialMaxUnpooling:__init(poolingModule)
parent.__init(self)
assert(torch.type(poolingModule)=='nn.SpatialMaxPooling', 'Argument must be a nn.SpatialMaxPooling module')
assert(poolingModule.kH==poolingModule.dH and poolingModule.kW==poolingModule.dW, "The size of pooling module's kernel must be equal to its stride")
self.pooling = poolingModule
end
function SpatialMaxUnpooling:setParams()
self.indices = self.pooling.indices
self.oheight = self.pooling.iheight
self.owidth = self.pooling.iwidth
end
function SpatialMaxUnpooling:updateOutput(input)
self:setParams()
input.THNN.SpatialMaxUnpooling_updateOutput(
input:cdata(),
self.output:cdata(),
self.indices:cdata(),
self.owidth, self.oheight
)
return self.output
end
function SpatialMaxUnpooling:updateGradInput(input, gradOutput)
self:setParams()
input.THNN.SpatialMaxUnpooling_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.indices:cdata(),
self.owidth, self.oheight
)
return self.gradInput
end
function SpatialMaxUnpooling:empty()
self:clearState()
end
function SpatialMaxUnpooling:__tostring__()
return 'nn.SpatialMaxUnpooling associated to '..tostring(self.pooling)
end
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