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----------------------------------------------------------------------
-- This script demonstrates how to load the Face Detector
-- training data, and pre-process it to facilitate learning.
--
-- It's a good idea to run this script with the interactive mode:
-- $ torch -i 1_data.lua
-- this will give you a Torch interpreter at the end, that you
-- can use to analyze/visualize the data you've just loaded.
--
-- Clement Farabet, Eugenio Culurciello
-- Mon Oct 14 14:58:50 EDT 2013
----------------------------------------------------------------------
require 'torch' -- torch
require 'image' -- to visualize the dataset
require 'nnx' -- provides a normalization operator
local opt = opt or {
visualize = false,
size = 'small',
patches='all'
}
----------------------------------------------------------------------
print(sys.COLORS.red .. '==> loading dataset')
function file_exists(name)
local f=io.open(name,"r")
if f~=nil then io.close(f) return true else return false end
end
train_dir = '..'
print("training directory "..train_dir)
if not file_exists(train_dir .. '/train_normalized_cifar10.t7') then
print("Please generate normalized input data using java examples")
os.exit(1)
end
local trainset = torch.load(train_dir..'/train_normalized_cifar10.t7')
local testset = torch.load(train_dir..'/test_normalized_cifar10.t7')
local classes = {'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'}
print("Train set size: " .. trainset.data:size(1) .. " test set size " .. testset.data:size(1))
print(" set size: " .. trainset.label:storage():size() .. " test set size " .. testset.label:storage():size())
setmetatable(trainset,
{__index = function(t, i)
return {t.data[i], t.label[i]}
end}
);
function trainset:size()
return self.data:size(1)
end
function testset:size()
return self.data:size(1)
end
function reduceData( dataHolder , fraction)
local dataShuffle = torch.randperm(dataHolder:size(1))
local smallSize = torch.floor(dataHolder:size(1)*fraction)
local tmp = {}
local labelStorage = dataHolder.label:storage()
tmp.data = torch.Tensor(smallSize, dataHolder.data:size(2), dataHolder.data:size(3), dataHolder.data:size(4))
tmp.label = torch.Tensor(smallSize)
for i=1,smallSize do
tmp.data[i] = dataHolder.data[dataShuffle[i]]
tmp.label[i] = labelStorage[dataShuffle[i]]
end
function tmp:size()
return self.data:size(1)
end
return tmp
end
-- Should be 1 indexed not 0
trainset.label:add(1)
testset.label:add(1)
-- Adjust size of training set based on request
if opt.size == 'small' then
trainset = reduceData(trainset,0.1)
testset = reduceData(testset,0.15)
print("Reduced size of sets to ",trainset:size()," ",testset:size())
end
-- other parts of the code uses labels not label
trainset.labels = trainset.label
testset.labels = testset.label
-- Exports
return {
trainData = trainset,
testData = testset,
classes = classes
}
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