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local Bilinear, parent = torch.class('nn.Bilinear', 'nn.Module')
local function isint(x) return type(x) == 'number' and x == math.floor(x) end
function Bilinear:__assertInput(input)
assert(input and torch.type(input) == 'table' and #input == 2,
'input should be a table containing two data Tensors')
assert(input[1]:nDimension() == 2 and input[2]:nDimension() == 2,
'input Tensors should be two-dimensional')
assert(input[1]:size(1) == input[2]:size(1),
'input Tensors should have the same number of rows (instances)')
assert(input[1]:size(2) == self.weight:size(2),
'dimensionality of first input is erroneous')
assert(input[2]:size(2) == self.weight:size(3),
'dimensionality of second input is erroneous')
end
function Bilinear:__assertInputGradOutput(input, gradOutput)
assert(input[1]:size(1) == gradOutput:size(1),
'number of rows in gradOutput does not match input')
assert(gradOutput:size(2) == self.weight:size(1),
'number of columns in gradOutput does not output size of layer')
end
function Bilinear:__init(inputSize1, inputSize2, outputSize, bias)
-- assertions:
assert(self and inputSize1 and inputSize2 and outputSize,
'should specify inputSize1 and inputSize2 and outputSize')
assert(isint(inputSize1) and isint(inputSize2) and isint(outputSize),
'inputSize1 and inputSize2 and outputSize should be integer numbers')
assert(inputSize1 > 0 and inputSize2 > 0 and outputSize > 0,
'inputSize1 and inputSize2 and outputSize should be positive numbers')
-- set up model:
parent.__init(self)
local bias = ((bias == nil) and true) or bias
self.weight = torch.Tensor(outputSize, inputSize1, inputSize2)
self.gradWeight = torch.Tensor(outputSize, inputSize1, inputSize2)
if bias then
self.bias = torch.Tensor(outputSize)
self.gradBias = torch.Tensor(outputSize)
end
self.gradInput = {torch.Tensor(), torch.Tensor()}
self:reset()
end
function Bilinear:reset(stdv)
assert(self)
if stdv then
assert(stdv and type(stdv) == 'number' and stdv > 0,
'standard deviation should be a positive number')
stdv = stdv * math.sqrt(3)
else
stdv = 1 / math.sqrt(self.weight:size(2))
end
self.weight:uniform(-stdv, stdv)
if self.bias then self.bias:uniform(-stdv, stdv) end
return self
end
function Bilinear:updateOutput(input)
assert(self)
self:__assertInput(input)
-- set up buffer:
self.buff2 = self.buff2 or input[1].new()
self.buff2:resizeAs(input[2])
-- compute output scores:
self.output:resize(input[1]:size(1), self.weight:size(1))
for k = 1,self.weight:size(1) do
torch.mm(self.buff2, input[1], self.weight[k])
self.buff2:cmul(input[2])
torch.sum(self.output:narrow(2, k, 1), self.buff2, 2)
end
if self.bias then
self.output:add(
self.bias:reshape(1, self.bias:nElement()):expandAs(self.output)
)
end
return self.output
end
function Bilinear:updateGradInput(input, gradOutput)
assert(self)
if self.gradInput then
self:__assertInputGradOutput(input, gradOutput)
if #self.gradInput == 0 then
for i = 1, 2 do self.gradInput[i] = input[1].new() end
end
-- compute d output / d input:
self.gradInput[1]:resizeAs(input[1]):fill(0)
self.gradInput[2]:resizeAs(input[2]):fill(0)
-- do first slice of weight tensor (k = 1)
self.gradInput[1]:mm(input[2], self.weight[1]:t())
self.gradInput[1]:cmul(gradOutput:narrow(2,1,1):expand(self.gradInput[1]:size(1),
self.gradInput[1]:size(2)))
self.gradInput[2]:addmm(1, input[1], self.weight[1])
self.gradInput[2]:cmul(gradOutput:narrow(2,1,1):expand(self.gradInput[2]:size(1),
self.gradInput[2]:size(2)))
-- do remaining slices of weight tensor
if self.weight:size(1) > 1 then
self.buff1 = self.buff1 or input[1].new()
self.buff1:resizeAs(input[1])
for k = 2, self.weight:size(1) do
self.buff1:mm(input[2], self.weight[k]:t())
self.buff1:cmul(gradOutput:narrow(2,k,1):expand(self.gradInput[1]:size(1),
self.gradInput[1]:size(2)))
self.gradInput[1]:add(self.buff1)
self.buff2:mm(input[1], self.weight[k])
self.buff2:cmul(gradOutput:narrow(2,k,1):expand(self.gradInput[2]:size(1),
self.gradInput[2]:size(2)))
self.gradInput[2]:add(self.buff2)
end
end
return self.gradInput
end
end
function Bilinear:accGradParameters(input, gradOutput, scale)
local scale = scale or 1
self:__assertInputGradOutput(input, gradOutput)
assert(scale and type(scale) == 'number' and scale >= 0)
-- make sure we have buffer:
self.buff1 = self.buff1 or input[1].new()
self.buff1:resizeAs(input[1])
-- accumulate parameter gradients:
for k = 1,self.weight:size(1) do
torch.cmul(
self.buff1, input[1], gradOutput:narrow(2, k, 1):expandAs(input[1])
)
self.gradWeight[k]:addmm(self.buff1:t(), input[2])
end
if self.bias then self.gradBias:add(scale, gradOutput:sum(1)) end
end
function Bilinear:sharedAccUpdateGradParameters(input, gradOutput, lr)
-- we do not need to accumulate parameters when sharing:
self:defaultAccUpdateGradParameters(input, gradOutput, lr)
end
function Bilinear:__tostring__()
return torch.type(self) ..
string.format(
'(%dx%d -> %d) %s',
self.weight:size(2), self.weight:size(3), self.weight:size(1),
(self.bias == nil and ' without bias' or '')
)
end
function Bilinear:clearState()
if self.buff2 then self.buff2:set() end
if self.buff1 then self.buff1:set() end
return parent.clearState(self)
end
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