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--------------------------------------------------------------------------------
-- DataList: a container for plain DataSets.
-- Each sub dataset represents one class.
--
-- Authors: Corda, Farabet
--------------------------------------------------------------------------------
local DataList, parent = torch.class('nn.DataList','nn.DataSet')
function DataList:__init()
parent.__init(self)
self.datasets = {}
self.nbClass = 0
self.ClassName = {}
self.nbSamples = 0
end
function DataList:__tostring__()
str = 'DataList:\n'
str = str .. ' + nb samples : '..self.nbSamples..'\n'
str = str .. ' + nb classes : '..self.nbClass
return str
end
function DataList:__index__(key)
if type(key)=='number' and self.nbClass>0 and key <= self.nbSamples then
local class = ((key-1) % self.nbClass) + 1
local classSize = self.datasets[class]:size()
local elmt = math.floor((key-1)/self.nbClass) + 1
elmt = ((elmt-1) % classSize) + 1
-- create target vector on the fly
self.datasets[class][elmt][2] = torch.Tensor(1,1,self.nbClass):fill(-1)
self.datasets[class][elmt][2][1][1][class] = 1
-- apply hook on sample
local sample = self.datasets[class][elmt]
if self.hookOnSample then
sample = self.hookOnSample(self,sample)
end
return sample,true
end
-- if key is not a number this should return nil
return rawget(self, key)
end
function DataList:appendDataSet(dataSet,className)
table.insert(self.datasets,dataSet)
if self.nbSamples == 0 then
self.nbSamples = dataSet:size()
else
self.nbSamples = math.floor(math.max(self.nbSamples/self.nbClass,dataSet:size()))
end
self.nbClass = self.nbClass + 1
self.nbSamples = self.nbSamples * self.nbClass
table.insert(self.ClassName,self.nbClass,className)
end
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