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------------------------------------------------------------------------
--[[ NaN ]]--
-- Asserts that outputs and gradInputs do not contain NaNs.
-- Useful for locating the source of NaN errors.
------------------------------------------------------------------------
local NaN, parent = torch.class("nn.NaN", "nn.Decorator")
local idseq = 0
function NaN.newId()
idseq = idseq + 1
return idseq
end
function NaN:__init(module, id)
parent.__init(self, module)
self.id = id or NaN.newId()
end
function NaN:recursiveIsNaN(tensor)
local isNaN = false
if torch.type(tensor) == 'table' then
for k,v in pairs(tensor) do
isNaN = self:recursiveIsNaN(v)
if isNaN then break end
end
else
local _ = require 'moses'
isNaN = _.isNaN(tensor:sum())
end
return isNaN
end
function NaN:updateOutput(input)
self.output = self.modules[1]:updateOutput(input)
if self:recursiveIsNaN(self.output) then
if self:recursiveIsNaN(input) then
error(string.format("NaN found in input of module :\n%s", self:__tostring__()))
elseif self:recursiveIsNaN(self:parameters()) then
error(string.format("NaN found in parameters of module :\n%s", self:__tostring__()))
end
error(string.format("NaN found in output of module :\n%s", self:__tostring__()))
end
return self.output
end
function NaN:updateGradInput(input, gradOutput)
self.gradInput = self.modules[1]:updateGradInput(input, gradOutput)
if self:recursiveIsNaN(self.gradInput) then
if self:recursiveIsNaN(gradOutput) then
error(string.format("NaN found in gradOutput of module :\n%s", self:__tostring__()))
end
error(string.format("NaN found in gradInput of module :\n%s", self:__tostring__()))
end
return self.gradInput
end
function NaN:accGradParameters(input, gradOutput, scale)
self.modules[1]:accGradParameters(input, gradOutput, scale)
local params, gradParams = self:parameters()
if self:recursiveIsNaN(gradParams) then
error(string.format("NaN found in gradParameters of module :\n%s", self:__tostring__()))
end
end
function NaN:__tostring__()
local selfstring = torch.type(self) .. '(' .. self.id .. ')'
if self.modules[1].__tostring__ then
return selfstring .. ' @ ' .. self.modules[1]:__tostring__()
else
return selfstring .. ' @ ' .. torch.type(self.modules[1])
end
end
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