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local SpatialAdaptiveAveragePooling, parent = torch.class('nn.SpatialAdaptiveAveragePooling', 'nn.Module')
function SpatialAdaptiveAveragePooling:__init(W, H)
parent.__init(self)
self.W = W
self.H = H
end
function SpatialAdaptiveAveragePooling:updateOutput(input)
input.THNN.SpatialAdaptiveAveragePooling_updateOutput(
input:cdata(),
self.output:cdata(),
self.W, self.H
)
return self.output
end
function SpatialAdaptiveAveragePooling:updateGradInput(input, gradOutput)
input.THNN.SpatialAdaptiveAveragePooling_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata()
)
return self.gradInput
end
-- for backward compat
function SpatialAdaptiveAveragePooling:empty()
self:clearState()
end
function SpatialAdaptiveAveragePooling:clearState()
return parent.clearState(self)
end
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