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local THNN = require 'nn.THNN'
local SpatialDilatedMaxPooling, parent = torch.class('nn.SpatialDilatedMaxPooling', 'nn.SpatialMaxPooling')
function SpatialDilatedMaxPooling:__init(kW, kH, dW, dH, padW, padH, dilationW, dilationH)
parent.__init(self, kW, kH, dW, dH, padW, padH)
self.dilationW = dilationW or 1
self.dilationH = dilationH or 1
end
function SpatialDilatedMaxPooling: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)
input.THNN.SpatialDilatedMaxPooling_updateOutput(
input:cdata(),
self.output:cdata(),
self.indices:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
self.dilationW, self.dilationH,
self.ceil_mode
)
return self.output
end
function SpatialDilatedMaxPooling:updateGradInput(input, gradOutput)
input.THNN.SpatialDilatedMaxPooling_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.indices:cdata(),
self.kW, self.kH,
self.dW, self.dH,
self.padW, self.padH,
self.dilationW, self.dilationH,
self.ceil_mode
)
return self.gradInput
end
function SpatialDilatedMaxPooling:__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 .. ', ' .. self.dilationW .. ',' .. self.dilationH
s = s .. ')'
return s
end
function SpatialDilatedMaxPooling:clearState()
if self.indices then
self.indices:set()
end
return parent.clearState(self)
end
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