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local CosineEmbeddingCriterion, parent = torch.class('nn.CosineEmbeddingCriterion', 'nn.Criterion')
function CosineEmbeddingCriterion:__init(margin)
parent.__init(self)
margin = margin or 0
self.margin = margin
self.gradInput = {torch.Tensor(), torch.Tensor()}
self.sizeAverage = true
end
function CosineEmbeddingCriterion:updateOutput(input,y)
local input1, input2 = input[1], input[2]
-- keep backward compatibility
if type(y) == 'number' then
self._y = self._y or input1.new(1)
self._y[1] = y
y = self._y
end
if input1:dim() == 1 then
input1 = input1:view(1,-1)
input2 = input2:view(1,-1)
end
if not self.buffer then
self.buffer = input1.new()
self.w1 = input1.new()
self.w22 = input1.new()
self.w = input1.new()
self.w32 = input1.new()
self._outputs = input1.new()
-- comparison operators behave differently from cuda/c implementations
if input1:type() == 'torch.CudaTensor' then
self._idx = input1.new()
else
self._idx = torch.ByteTensor()
end
end
self.buffer:cmul(input1,input2)
self.w1:sum(self.buffer,2)
local epsilon = 1e-12
self.buffer:cmul(input1,input1)
self.w22:sum(self.buffer,2):add(epsilon)
-- self._outputs is also used as a temporary buffer
self._outputs:resizeAs(self.w22):fill(1)
self.w22:cdiv(self._outputs, self.w22)
self.w:resizeAs(self.w22):copy(self.w22)
self.buffer:cmul(input2,input2)
self.w32:sum(self.buffer,2):add(epsilon)
self.w32:cdiv(self._outputs, self.w32)
self.w:cmul(self.w32)
self.w:sqrt()
self._outputs:cmul(self.w1,self.w)
self._outputs = self._outputs:select(2,1)
y.eq(self._idx,y,-1)
self._outputs[self._idx] = self._outputs[self._idx]:add(-self.margin):cmax(0)
y.eq(self._idx,y,1)
self._outputs[self._idx] = self._outputs[self._idx]:mul(-1):add(1)
self.output = self._outputs:sum()
if self.sizeAverage then
self.output = self.output/y:size(1)
end
return self.output
end
function CosineEmbeddingCriterion:updateGradInput(input, y)
local v1 = input[1]
local v2 = input[2]
local not_batch = false
-- keep backward compatibility
if type(y) == 'number' then
self._y = self._y or input1.new(1)
self._y[1] = y
y = self._y
end
if v1:dim() == 1 then
v1 = v1:view(1,-1)
v2 = v2:view(1,-1)
not_batch = true
end
local gw1 = self.gradInput[1]
local gw2 = self.gradInput[2]
gw1:resizeAs(v1):copy(v2)
gw2:resizeAs(v1):copy(v1)
self.buffer:cmul(self.w1,self.w22)
gw1:addcmul(-1,self.buffer:expandAs(v1),v1)
gw1:cmul(self.w:expandAs(v1))
self.buffer:cmul(self.w1,self.w32)
gw2:addcmul(-1,self.buffer:expandAs(v1),v2)
gw2:cmul(self.w:expandAs(v1))
-- self._idx = self._outputs <= 0
y.le(self._idx,self._outputs,0)
self._idx = self._idx:view(-1,1):expand(gw1:size())
gw1[self._idx] = 0
gw2[self._idx] = 0
y.eq(self._idx,y,1)
self._idx = self._idx:view(-1,1):expand(gw2:size())
gw1[self._idx] = gw1[self._idx]:mul(-1)
gw2[self._idx] = gw2[self._idx]:mul(-1)
if self.sizeAverage then
gw1:div(y:size(1))
gw2:div(y:size(1))
end
if not_batch then
self.gradInput[1]:resize(gw1:size(2))
self.gradInput[2]:resize(gw2:size(2))
end
return self.gradInput
end
function CosineEmbeddingCriterion:type(type)
self._idx = nil
parent.type(self,type)
-- comparison operators behave differently from cuda/c implementations
if type == 'torch.CudaTensor' then
self._idx = torch.CudaTensor()
else
self._idx = torch.ByteTensor()
end
return self
end
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