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local WeightedEuclidean, parent = torch.class('nn.WeightedEuclidean', 'nn.Module')
function WeightedEuclidean:__init(inputSize,outputSize)
parent.__init(self)
self.weight = torch.Tensor(inputSize,outputSize)
self.gradWeight = torch.Tensor(inputSize,outputSize)
-- each template (output dim) has its own diagonal covariance matrix
self.diagCov = torch.Tensor(inputSize,outputSize)
self.gradDiagCov = torch.Tensor(inputSize,outputSize)
self:reset()
end
function WeightedEuclidean:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1./math.sqrt(self.weight:size(1))
end
self.weight:uniform(-stdv, stdv)
self.diagCov:fill(1)
end
local function view(res, src, ...)
local args = {...}
if src:isContiguous() then
res:view(src, table.unpack(args))
else
res:reshape(src, table.unpack(args))
end
end
function WeightedEuclidean:updateOutput(input)
-- lazy-initialize
self._diagCov = self._diagCov or self.output.new()
self._input = self._input or input.new()
self._weight = self._weight or self.weight.new()
self._expand = self._expand or self.output.new()
self._expand2 = self._expand or self.output.new()
self._expand3 = self._expand3 or self.output.new()
self._repeat = self._repeat or self.output.new()
self._repeat2 = self._repeat2 or self.output.new()
self._repeat3 = self._repeat3 or self.output.new()
local inputSize, outputSize = self.weight:size(1), self.weight:size(2)
-- y_j = || c_j * (w_j - x) ||
if input:dim() == 1 then
view(self._input, input, inputSize, 1)
self._expand:expandAs(self._input, self.weight)
self._repeat:resizeAs(self._expand):copy(self._expand)
self._repeat:add(-1, self.weight)
self._repeat:cmul(self.diagCov)
self.output:norm(self._repeat, 2, 1)
self.output:resize(outputSize)
elseif input:dim() == 2 then
local batchSize = input:size(1)
view(self._input, input, batchSize, inputSize, 1)
self._expand:expand(self._input, batchSize, inputSize, outputSize)
-- make the expanded tensor contiguous (requires lots of memory)
self._repeat:resizeAs(self._expand):copy(self._expand)
self._weight:view(self.weight, 1, inputSize, outputSize)
self._expand2:expandAs(self._weight, self._repeat)
self._diagCov:view(self.diagCov, 1, inputSize, outputSize)
self._expand3:expandAs(self._diagCov, self._repeat)
if torch.type(input) == 'torch.CudaTensor' then
-- requires lots of memory, but minimizes cudaMallocs and loops
self._repeat2:resizeAs(self._expand2):copy(self._expand2)
self._repeat:add(-1, self._repeat2)
self._repeat3:resizeAs(self._expand3):copy(self._expand3)
self._repeat:cmul(self._repeat3)
else
self._repeat:add(-1, self._expand2)
self._repeat:cmul(self._expand3)
end
self.output:norm(self._repeat, 2, 2)
self.output:resize(batchSize, outputSize)
else
error"1D or 2D input expected"
end
return self.output
end
function WeightedEuclidean:updateGradInput(input, gradOutput)
if not self.gradInput then
return
end
self._div = self._div or input.new()
self._output = self._output or self.output.new()
self._expand4 = self._expand4 or input.new()
self._gradOutput = self._gradOutput or input.new()
if not self.fastBackward then
self:updateOutput(input)
end
local inputSize, outputSize = self.weight:size(1), self.weight:size(2)
--[[
dy_j -2 * c_j * c_j * (w_j - x) c_j * c_j * (x - w_j)
---- = -------------------------- = ---------------------
dx 2 || c_j * (w_j - x) || y_j
--]]
-- to prevent div by zero (NaN) bugs
self._output:resizeAs(self.output):copy(self.output):add(0.0000001)
view(self._gradOutput, gradOutput, gradOutput:size())
self._div:cdiv(gradOutput, self._output)
if input:dim() == 1 then
self._div:resize(1, outputSize)
self._expand4:expandAs(self._div, self.weight)
if torch.type(input) == 'torch.CudaTensor' then
self._repeat2:resizeAs(self._expand4):copy(self._expand4)
self._repeat2:cmul(self._repeat)
else
self._repeat2:cmul(self._repeat, self._expand4)
end
self._repeat2:cmul(self.diagCov)
self.gradInput:sum(self._repeat2, 2)
self.gradInput:resizeAs(input)
elseif input:dim() == 2 then
local batchSize = input:size(1)
self._div:resize(batchSize, 1, outputSize)
self._expand4:expand(self._div, batchSize, inputSize, outputSize)
if torch.type(input) == 'torch.CudaTensor' then
self._repeat2:resizeAs(self._expand4):copy(self._expand4)
self._repeat2:cmul(self._repeat)
self._repeat2:cmul(self._repeat3)
else
self._repeat2:cmul(self._repeat, self._expand4)
self._repeat2:cmul(self._expand3)
end
self.gradInput:sum(self._repeat2, 3)
self.gradInput:resizeAs(input)
else
error"1D or 2D input expected"
end
return self.gradInput
end
function WeightedEuclidean:accGradParameters(input, gradOutput, scale)
local inputSize, outputSize = self.weight:size(1), self.weight:size(2)
scale = scale or 1
--[[
dy_j 2 * c_j * c_j * (w_j - x) c_j * c_j * (w_j - x)
---- = ------------------------- = ---------------------
dw_j 2 || c_j * (w_j - x) || y_j
dy_j 2 * c_j * (w_j - x)^2 c_j * (w_j - x)^2
---- = ----------------------- = -----------------
dc_j 2 || c_j * (w_j - x) || y_j
--]]
-- assumes a preceding call to updateGradInput
if input:dim() == 1 then
self.gradWeight:add(-scale, self._repeat2)
self._repeat:cdiv(self.diagCov)
self._repeat:cmul(self._repeat)
self._repeat:cmul(self.diagCov)
if torch.type(input) == 'torch.CudaTensor' then
self._repeat2:resizeAs(self._expand4):copy(self._expand4)
self._repeat2:cmul(self._repeat)
else
self._repeat2:cmul(self._repeat, self._expand4)
end
self.gradDiagCov:add(self._repeat2)
elseif input:dim() == 2 then
self._sum = self._sum or input.new()
self._sum:sum(self._repeat2, 1)
self._sum:resize(inputSize, outputSize)
self.gradWeight:add(-scale, self._sum)
if torch.type(input) == 'torch.CudaTensor' then
-- requires lots of memory, but minimizes cudaMallocs and loops
self._repeat:cdiv(self._repeat3)
self._repeat:cmul(self._repeat)
self._repeat:cmul(self._repeat3)
self._repeat2:resizeAs(self._expand4):copy(self._expand4)
self._repeat:cmul(self._repeat2)
else
self._repeat:cdiv(self._expand3)
self._repeat:cmul(self._repeat)
self._repeat:cmul(self._expand3)
self._repeat:cmul(self._expand4)
end
self._sum:sum(self._repeat, 1)
self._sum:resize(inputSize, outputSize)
self.gradDiagCov:add(scale, self._sum)
else
error"1D or 2D input expected"
end
end
function WeightedEuclidean:type(type, tensorCache)
if type then
-- prevent premature memory allocations
self._input = nil
self._output = nil
self._gradOutput = nil
self._weight = nil
self._div = nil
self._sum = nil
self._expand = nil
self._expand2 = nil
self._expand3 = nil
self._expand4 = nil
self._repeat = nil
self._repeat2 = nil
self._repeat3 = nil
end
return parent.type(self, type, tensorCache)
end
function WeightedEuclidean:parameters()
return {self.weight, self.diagCov}, {self.gradWeight, self.gradDiagCov}
end
function WeightedEuclidean:accUpdateGradParameters(input, gradOutput, lr)
local gradWeight = self.gradWeight
local gradDiagCov = self.gradDiagCov
self.gradWeight = self.weight
self.gradDiagCov = self.diagCov
self:accGradParameters(input, gradOutput, -lr)
self.gradWeight = gradWeight
self.gradDiagCov = gradDiagCov
end
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