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SpatialMaxPooling.lua
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local SpatialMaxPooling, parent = torch.class('nn.SpatialMaxPooling', 'nn.Module')
function SpatialMaxPooling:__init(kW, kH, dW, dH, padW, padH)
parent.__init(self)
dW = dW or kW
dH = dH or kH
self.kW = kW
self.kH = kH
self.dW = dW
self.dH = dH
self.padW = padW or 0
self.padH = padH or 0
self.ceil_mode = false
self.indices = torch.LongTensor()
end
function SpatialMaxPooling:ceil()
self.ceil_mode = true
return self
end
function SpatialMaxPooling:floor()
self.ceil_mode = false
return self
end
function SpatialMaxPooling:updateOutput(input)
self.indices = self.indices or torch.LongTensor()
if torch.typename(input):find('torch%.Cuda.*Tensor') then
self.indices = torch.CudaLongTensor and self.indices:cudaLong() or self.indices
else
self.indices = self.indices:long()
end
local dims = input:dim()
self.iheight = input:size(dims-1)
self.iwidth = input:size(dims)
-- backward compatibility
self.ceil_mode = self.ceil_mode or false
self.padW = self.padW or 0
self.padH = self.padH or 0
input.THNN.SpatialMaxPooling_updateOutput(
input:cdata(),
self.output:cdata(),
self.indices:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
self.ceil_mode
)
return self.output
end
function SpatialMaxPooling:updateGradInput(input, gradOutput)
input.THNN.SpatialMaxPooling_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.indices:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
self.ceil_mode
)
return self.gradInput
end
-- for backward compat
function SpatialMaxPooling:empty()
self:clearState()
end
function SpatialMaxPooling:__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
function SpatialMaxPooling:clearState()
if self.indices then
self.indices:set()
end
return parent.clearState(self)
end