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Data.lua
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Data.lua
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--[[
This code create the training test and validation datasets and preform diffrent kinds of preprocessing
This code is based on elad hoffer Data.lua file from ConvNet-torch library (https://github.com/eladhoffer/ConvNet-torch.git) and uses:
- Elad Hoffer DataProvidor.torch library: https://github.com/eladhoffer/DataProvider.torch.git
- Nicholas Leonard dp library: https://github.com/nicholas-leonard/dp.git
- Koray Kavukcuoglu dp library: https://github.com/koraykv/unsup.git
]]
require 'dp'
local DataProvider = require 'DataProvider'
local opt = opt or {}
local Dataset = opt.dataset or 'Cifar10'
local PreProcDir = opt.preProcDir or './PreProcData/'
local Whiten = opt.whiten or false
local NormelizeWhiten = opt.NormelizeWhiten or false
local DataPath = opt.datapath or '/home/itayh/Datasets/'
local normalization = opt.normalization or 'simple'
local format = opt.format or 'rgb'
local TestData
local TrainData
local ValidData
local Classes
if Dataset =='Cifar100' then
local file_valid = paths.concat(PreProcDir, format .. 'whiten_valid.t7')
local file_train = paths.concat(PreProcDir, format .. 'whiten_train.t7')
local file_test = paths.concat(PreProcDir, format .. 'whiten_test.t7')
if (paths.filep(file_valid) and paths.filep(file_train) and paths.filep(file_test)) then
ValidData=torch.load(file_valid)
TrainData=torch.load(file_train)
TestData=torch.load(file_test)
else
if paths.dirp(PreProcDir)==false then
sys.execute('mkdir PreProcData/Cifar100')
end
input_preprocess = {}
table.insert(input_preprocess, dp.ZCA())
ds = dp.Cifar100{scale={0,1}, valid_ratio=0.1,input_preprocess = input_preprocess}
ValidData = {data=ds:validSet():inputs():input():clone():float(), label=ds:validSet():targets():input():clone():byte() }
TrainData = {data=ds:trainSet():inputs():input():float(), label=ds:trainSet():targets():input():byte() }
TestData = {data=ds:testSet():inputs():input():float() , label=ds:testSet():targets():input():byte() }
collectgarbage()
torch.save(file_valid,ValidData)
torch.save(file_train,TrainData)
torch.save(file_test,TestData)
end
elseif Dataset == 'Cifar10' then
local file_valid = paths.concat(PreProcDir, format .. 'whiten_valid.t7')
local file_train = paths.concat(PreProcDir, format .. 'whiten_train.t7')
local file_test = paths.concat(PreProcDir, format .. 'whiten_test.t7')
if (paths.filep(file_valid) and paths.filep(file_train) and paths.filep(file_test)) then
ValidData=torch.load(file_valid)
TrainData=torch.load(file_train)
TestData=torch.load(file_test)
else
if paths.dirp(PreProcDir)==false then
sys.execute('mkdir PreProcData/Cifar10')
end
input_preprocess = {}
table.insert(input_preprocess, dp.ZCA())
ds = dp.Cifar10{scale={0,1},valid_ratio=0.1,input_preprocess = input_preprocess} --,input_preprocess = input_preprocess} scale={0,1},
ValidData = {data=ds:validSet():inputs():input():float(), label=ds:validSet():targets():input():clone():byte() }
TrainData = {data=ds:trainSet():inputs():input():float(), label=ds:trainSet():targets():input():byte() }
TestData = {data=ds:testSet():inputs():input():float(), label=ds:testSet():targets():input():byte() }
collectgarbage()
torch.save(file_valid,ValidData)
torch.save(file_train,TrainData)
torch.save(file_test,TestData)
end
Classes = {'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'}
elseif Dataset == 'MNIST' then
local file_valid = paths.concat(PreProcDir, format .. '_valid.t7')
local file_train = paths.concat(PreProcDir, format .. '_train.t7')
local file_test = paths.concat(PreProcDir, format .. '_test.t7')
if (paths.filep(file_valid) and paths.filep(file_train) and paths.filep(file_test)) then
ValidData=torch.load(file_valid)
TrainData=torch.load(file_train)
TestData=torch.load(file_test)
else
if paths.dirp(PreProcDir)==false then
sys.execute('mkdir PreProcData/MNIST')
end
ds = dp.Mnist{scale={0,1}}
ValidData = {data=ds:validSet():inputs():input():clone():float(), label=ds:validSet():targets():input():clone():byte() }
TrainData = {data=ds:trainSet():inputs():input():float(), label=ds:trainSet():targets():input():byte() }
TestData = {data=ds:testSet():inputs():input():float() , label=ds:testSet():targets():input():byte() }
collectgarbage()
torch.save(file_valid,ValidData)
torch.save(file_train,TrainData)
torch.save(file_test,TestData)
end
Classes = {1,2,3,4,5,6,7,8,9,0}
elseif Dataset == 'SVHN' then
local LCNfile_valid = paths.concat(PreProcDir, format .. 'GCN_LCN_valid.t7')
local LCNfile_train = paths.concat(PreProcDir, format .. 'GCN_LCN_train.t7')
local LCNfile_test = paths.concat(PreProcDir, format .. 'GCN_LCN_test.t7')
print(LCNfile_valid)
if (paths.filep(LCNfile_valid) and paths.filep(LCNfile_train) and paths.filep(LCNfile_test)) then
ValidData=torch.load(LCNfile_valid)
TrainData=torch.load(LCNfile_train)
TestData=torch.load(LCNfile_test)
else
if paths.dirp(PreProcDir)==false then
sys.execute('mkdir PreProcData/SVHN')
end
local input_preprocess = {}
table.insert(input_preprocess, dp.GCN{batch_size=5000,use_std=true,sqrt_bias=10})
table.insert(input_preprocess, dp.LeCunLCN{kernel_size=9,divide_by_std=true,batch_size=5000,progress=true}) --,kernel_size=31,kernel_std=32})
ds = dp.Svhn{scale={0,1}, input_preprocess = input_preprocess}
ValidData = {data=ds:validSet():inputs():input():float(), label=ds:validSet():targets():input():byte() }; ValidData.data:div( ValidData.data:max())
TrainData = {data=ds:trainSet():inputs():input():float(), label=ds:trainSet():targets():input():byte() }; TrainData.data:div( TrainData.data:max())
TestData = {data=ds:testSet():inputs():input():float(), label=ds:testSet():targets():input():byte() }; TestData.data:div( TestData.data:max())
collectgarbage()
torch.save(LCNfile_valid,ValidData)
torch.save(LCNfile_train,TrainData)
torch.save(LCNfile_test,TestData)
end
Classes = {1,2,3,4,5,6,7,8,9,0}
end
TrainData.data = TrainData.data:float()
TestData.data = TestData.data:float()
local TrainDataProvider = DataProvider.Container{
Name = 'TrainingData',
CachePrefix = nil,
CacheFiles = false,
Source = {TrainData.data,TrainData.label},
MaxNumItems = 1e6,
CopyData = false,
TensorType = 'torch.FloatTensor',
}
local TestDataProvider = DataProvider.Container{
Name = 'TestData',
CachePrefix = nil,
CacheFiles = false,
Source = {TestData.data, TestData.label},
MaxNumItems = 1e6,
CopyData = false,
TensorType = 'torch.FloatTensor',
}
local ValidDataProvider = DataProvider.Container{
Name = 'ValidData',
CachePrefix = nil,
CacheFiles = false,
Source = {ValidData.data, ValidData.label},
MaxNumItems = 1e6,
CopyData = false,
TensorType = 'torch.FloatTensor',
}
--Preprocesss
if format == 'yuv' then
require 'image'
TrainDataProvider:apply(image.rgb2yuv)
TestDataProvider:apply(image.rgb2yuv)
end
if Whiten then
require 'unsup'
local meanfile = paths.concat(PreProcDir, format .. 'imageMean.t7')
local mean, P, invP
local Pfile = paths.concat(PreProcDir,format .. 'P.t7')
local invPfile = paths.concat(PreProcDir,format .. 'invP.t7')
if (paths.filep(Pfile) and paths.filep(invPfile) and paths.filep(meanfile)) then
P = torch.load(Pfile)
invP = torch.load(invPfile)
mean = torch.load(meanfile)
TrainDataProvider.Data = unsup.zca_whiten(TrainDataProvider.Data, mean, P, invP)
else
TrainDataProvider.Data, mean, P, invP = unsup.zca_whiten(TrainDataProvider.Data)
torch.save(Pfile,P)
torch.save(invPfile,invP)
torch.save(meanfile,mean)
end
TestDataProvider.Data = unsup.zca_whiten(TestDataProvider.Data, mean, P, invP)
ValidDataProvider.Data = unsup.zca_whiten(ValidDataProvider.Data, mean, P, invP)
elseif dp_prepro then
-- Do nothing since we use dp lib for GCN and LCN
else
local meanfile = paths.concat(PreProcDir, format .. normalization .. 'Mean.t7')
local stdfile = paths.concat(PreProcDir,format .. normalization .. 'Std.t7')
local mean, std
local loaded = false
if paths.filep(meanfile) and paths.filep(stdfile) then
mean = torch.load(meanfile)
std = torch.load(stdfile)
loaded = true
end
mean, std = TrainDataProvider:normalize(normalization, mean, std)
TestDataProvider:normalize(normalization, mean, std)
ValidDataProvider:normalize(normalization, mean, std)
if not loaded then
torch.save(meanfile,mean)
torch.save(stdfile,std)
end
end
return{
TrainData = TrainDataProvider,
TestData = TestDataProvider,
ValidData = ValidDataProvider,
Classes = Classes
}