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local LinearWeightNorm, parent = torch.class('nn.LinearWeightNorm', 'nn.Linear')
function LinearWeightNorm:__init(inputSize, outputSize, bias, eps)
nn.Module.__init(self) -- Skip nn.Linear constructor
local bias = ((bias == nil) and true) or bias
self.eps = eps or 1e-16
self.outputSize = outputSize
self.inputSize = inputSize
self.v = torch.Tensor(outputSize, inputSize)
self.gradV = torch.Tensor(outputSize, inputSize)
self.weight = torch.Tensor(outputSize, inputSize)
self.g = torch.Tensor(outputSize,1)
self.gradG = torch.Tensor(outputSize,1)
self.norm = torch.Tensor(outputSize,1)
self.scale = torch.Tensor(outputSize,1)
if bias then
self.bias = torch.Tensor(outputSize)
self.gradBias = torch.Tensor(outputSize)
end
self:reset()
end
function LinearWeightNorm:evaluate()
if self.train ~= false then
self:updateWeightMatrix()
end
parent.evaluate(self)
end
function LinearWeightNorm:initFromWeight(weight)
weight = weight or self.weight
self.g:norm(weight,2,2):clamp(self.eps,math.huge)
self.v:copy(weight)
return self
end
function LinearWeightNorm.fromLinear(linear)
local module = nn.LinearWeightNorm(linear.weight:size(2), linear.weight:size(1), torch.isTensor(linear.bias))
module.weight:copy(linear.weight)
module:initFromWeight()
if linear.bias then
module.bias:copy(linear.bias)
end
return module
end
function LinearWeightNorm:toLinear()
self:updateWeightMatrix()
local module = nn.Linear(self.inputSize, self.outputSize, torch.isTensor(self.bias))
module.weight:copy(self.weight)
if self.bias then
module.bias:copy(self.bias)
end
return module
end
function LinearWeightNorm:parameters()
if self.bias then
return {self.v, self.g, self.bias}, {self.gradV, self.gradG, self.gradBias}
else
return {self.v, self.g}, {self.gradV, self.gradG}
end
end
function LinearWeightNorm:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1 / math.sqrt(self.inputSize)
end
self.weight:uniform(-stdv,stdv)
self:initFromWeight()
if self.bias then
self.bias:uniform(-stdv,stdv)
end
end
function LinearWeightNorm:updateWeightMatrix()
if self.norm:dim() == 0 then self.norm:resizeAs(self.g) end
if self.scale:dim() == 0 then self.scale:resizeAs(self.g) end
if self.weight:dim() == 0 then self.weight:resizeAs(self.v) end
self.norm:norm(self.v,2,2):clamp(self.eps,math.huge)
self.scale:cdiv(self.g,self.norm)
self.weight:cmul(self.v,self.scale:expandAs(self.v))
end
function LinearWeightNorm:updateOutput(input)
if self.train ~= false then
self:updateWeightMatrix()
end
return parent.updateOutput(self, input)
end
function LinearWeightNorm:accGradParameters(input, gradOutput, scale)
scale = scale or 1
if input:dim() == 1 then
self.gradV:addr(scale, gradOutput, input)
if self.bias then self.gradBias:add(scale, gradOutput) end
elseif input:dim() == 2 then
self.gradV:addmm(scale, gradOutput:t(), input)
if self.bias then
-- update the size of addBuffer if the input is not the same size as the one we had in last updateGradInput
self:updateAddBuffer(input)
self.gradBias:addmv(scale, gradOutput:t(), self.addBuffer)
end
end
local scale = self.scale:expandAs(self.v)
local norm = self.norm:expandAs(self.v)
self.weight:cmul(self.gradV,self.v):cdiv(norm)
self.gradG:sum(self.weight,2)
self.gradV:cmul(scale)
self.weight:cmul(self.v,scale):cdiv(norm)
self.weight:cmul(self.gradG:expandAs(self.weight))
self.gradV:add(-1,self.weight)
end
function LinearWeightNorm:defaultAccUpdateGradParameters(input, gradOutput, lr)
local gradV = self.gradV
local gradG = self.gradG
local gradBias = self.gradBias
self.gradV = self.v
self.gradG = self.g
self.gradBias = self.bias
self:accGradParameters(input, gradOutput, -lr)
self.gradV = gradV
self.gradG = gradG
self.gradBias = gradBias
end
function LinearWeightNorm:clearState()
nn.utils.clear(self, 'weight', 'norm', 'scale')
return parent.clearState(self)
end
function LinearWeightNorm:__tostring__()
return torch.type(self) ..
string.format('(%d -> %d)', self.inputSize, self.outputSize) ..
(self.bias == nil and ' without bias' or '')
end
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