99 lines
2.9 KiB
Lua
99 lines
2.9 KiB
Lua
-- Source: https://github.com/soumith/imagenet-multiGPU.torch/blob/master/donkey.lua
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--
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-- Copyright (c) 2014, Facebook, Inc.
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-- All rights reserved.
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--
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-- This source code is licensed under the BSD-style license found in the
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-- LICENSE file in the root directory of this source tree. An additional grant
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-- of patent rights can be found in the PATENTS file in the same directory.
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--
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local gm = assert(require 'graphicsmagick')
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paths.dofile('dataset.lua')
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paths.dofile('util.lua')
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ffi=require 'ffi'
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-- This file contains the data-loading logic and details.
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-- It is run by each data-loader thread.
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------------------------------------------
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-- a cache file of the training metadata (if doesnt exist, will be created)
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local trainCache = paths.concat(opt.cache, 'trainCache.t7')
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local testCache = paths.concat(opt.cache, 'testCache.t7')
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-- Check for existence of opt.data
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if not os.execute('cd ' .. opt.data) then
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error(("could not chdir to '%s'"):format(opt.data))
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end
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local loadSize = {3, opt.imgDim, opt.imgDim}
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local sampleSize = {3, opt.imgDim, opt.imgDim}
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-- function to load the image, jitter it appropriately (random crops etc.)
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local trainHook = function(self, path)
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-- load image with size hints
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local input = gm.Image():load(path, self.loadSize[3], self.loadSize[2])
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input:size(self.sampleSize[3], self.sampleSize[2])
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local out = input
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-- do hflip with probability 0.5
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if torch.uniform() > 0.5 then out:flop(); end
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out = out:toTensor('float','RGB','DHW')
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return out
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end
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if paths.filep(trainCache) then
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print('Loading train metadata from cache')
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trainLoader = torch.load(trainCache)
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trainLoader.sampleHookTrain = trainHook
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else
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print('Creating train metadata')
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trainLoader = dataLoader{
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paths = {paths.concat(opt.data, 'train')},
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loadSize = loadSize,
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sampleSize = sampleSize,
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split = 100,
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verbose = true
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}
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torch.save(trainCache, trainLoader)
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trainLoader.sampleHookTrain = trainHook
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end
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collectgarbage()
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-- do some sanity checks on trainLoader
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do
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local class = trainLoader.imageClass
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local nClasses = #trainLoader.classes
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assert(class:max() <= nClasses, "class logic has error")
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assert(class:min() >= 1, "class logic has error")
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end
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-- End of train loader section
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--------------------------------------------------------------------------------
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--[[ Section 2: Create a test data loader (testLoader), ]]--
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if paths.filep(testCache) then
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print('Loading test metadata from cache')
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testLoader = torch.load(testCache)
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else
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print('Creating test metadata')
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testLoader = dataLoader{
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paths = {paths.concat(opt.data, 'val')},
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loadSize = loadSize,
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sampleSize = sampleSize,
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-- split = 0,
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split = 100,
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verbose = true,
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-- force consistent class indices between trainLoader and testLoader
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forceClasses = trainLoader.classes
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}
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torch.save(testCache, testLoader)
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end
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collectgarbage()
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-- End of test loader section
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