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local SpatialAveragePooling, parent = torch.class('nn.SpatialAveragePooling', 'nn.Module')
function SpatialAveragePooling:__init(kW, kH, dW, dH, padW, padH)
parent.__init(self)
self.kW = kW
self.kH = kH
self.dW = dW or 1
self.dH = dH or 1
self.padW = padW or 0
self.padH = padH or 0
self.ceil_mode = false
self.count_include_pad = true
self.divide = true
end
function SpatialAveragePooling:ceil()
self.ceil_mode = true
return self
end
function SpatialAveragePooling:floor()
self.ceil_mode = false
return self
end
function SpatialAveragePooling:setCountIncludePad()
self.count_include_pad = true
return self
end
function SpatialAveragePooling:setCountExcludePad()
self.count_include_pad = false
return self
end
local function backwardCompatible(self)
if self.ceil_mode == nil then
self.ceil_mode = false
self.count_include_pad = true
self.padH = 0
self.padW = 0
end
end
function SpatialAveragePooling:updateOutput(input)
backwardCompatible(self)
input.THNN.SpatialAveragePooling_updateOutput(
input:cdata(),
self.output:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
self.ceil_mode,
self.count_include_pad
)
-- for backward compatibility with saved models
-- which are not supposed to have "divide" field
if not self.divide then
self.output:mul(self.kW*self.kH)
end
return self.output
end
function SpatialAveragePooling:updateGradInput(input, gradOutput)
if self.gradInput then
input.THNN.SpatialAveragePooling_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
self.ceil_mode,
self.count_include_pad
)
-- for backward compatibility
if not self.divide then
self.gradInput:mul(self.kW*self.kH)
end
return self.gradInput
end
end
function SpatialAveragePooling:__tostring__()
local s = string.format('%s(%dx%d, %d,%d', torch.type(self),
self.kW, self.kH, self.dW, self.dH)
if (self.padW or self.padH) and (self.padW ~= 0 or self.padH ~= 0) then
s = s .. ', ' .. self.padW .. ','.. self.padH
end
s = s .. ')'
return s
end
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