loadImageNet.lua

local dl = require 'dataload._env'

-- load ILSVRC2012-14 image classification dataset (A.K.A. ImageNet)
-- http://image-net.org/challenges/LSVRC/2014/download-images-5jj5.php
-- Wraps the Large Scale Visual Recognition Challenge 2014 (ILSVRC2014)
-- classification dataset (commonly known as ImageNet). The dataset
-- hasn't changed from 2012-2014.
-- Due to its size, the data first needs to be prepared offline :
-- 1. use scripts/downloadimagenet.lua to download and extract the data
-- 2. use scripts/harmonizeimagenet.lua to harmonize train/valid sets
function dl.loadImageNet(datapath, nthread, loadsize, samplesize, verbose)
 -- 1. arguments and defaults
 
 -- path containing 3 folders : ILSVRC2012_img_train, ILSVRC2012_img_val and metadata
 assert(torch.type(datapath) == 'string' and paths.dirp(datapath), "Expecting path to ILSVRC2012 data at arg one") 
 -- number of threads to use per set
 nthread = nthread or 2
 -- approximate size to load the images to before cropping.
 loadsize = loadsize or {3, 256, 256}
 -- consistent size for cropped patches from loaded images.
 samplesize = samplesize or {3, 224, 224}
 -- verbose initialization
 verbose = verbose == nil and true or verbose

 -- path to training images
 local trainpath = paths.concat(datapath, 'ILSVRC2012_img_train')
 -- path to validation images
 local validpath = paths.concat(datapath, 'ILSVRC2012_img_val')
 -- path to meta data
 local metapath = paths.concat(datapath, 'metadata')
 
 local sortfunc = function(x,y)
 return tonumber(x:match('[0-9]+')) < tonumber(y:match('[0-9]+'))
 end
 
 local train = dl.ImageClass(trainpath, loadsize, samplesize, 'sampleTrain', sortfunc, verbose)
 local valid = dl.ImageClass(validpath, loadsize, samplesize, 'sampleTest', sortfunc, verbose)
 
 train = dl.AsyncIterator(train, nthread)
 valid = dl.AsyncIterator(valid, nthread)
 
 local classinfopath = paths.concat(metapath, 'classInfo.th7')
 if paths.filep(classinfopath) then
 local classinfo = torch.load(classinfopath)
 train.classinfo = classinfo
 valid.classinfo = classinfo
 else
 if verbose then
 print("ImageNet: skipping "..classnfopath)
 print("To avoid this message use harmonizeimagenet.lua "..
 "script and pass correct metapath")
 end
 end

 return train, valid
end