load.lua

require 'image'

-- Image Transformations
local M = {}

M.ColorNormalize = function()
  return function(img)
    img = img:clone()
    local x = meanstd
    for i=1,3 do
       img[i]:add(0.5)
       img[i]:div(10)
    end
    return img
  end
end

M.Scale = function(size)
         return function(input)
            local w, h = input:size(3), input:size(2)
            if (w <= h and w == size) or (h <= w and h == size) then
               return input
            end
            if w < h then
               return image.scale(input, size, h/w * size)
            else
               return image.scale(input, w/h * size, size)
            end
         end
      end

M.HorizontalFlip = function(prob)
         return function(input)
            if torch.uniform() < prob then
               input = image.hflip(input)
            end
            return input
         end
      end

M.RandomCrop = function(size)
         return function(input)

            local w, h = input:size(3), input:size(2)
            if w == size and h == size then
               return input
            end

            local x1, y1 = torch.random(0, w - size), torch.random(0, h - size)
            local out = image.crop(input, x1, y1, x1 + size, y1 + size)
            assert(out:size(2) == size and out:size(3) == size, 'wrong crop size')
            return out
         end
      end

-------------------------------------

image.save('temp.jpg',torch.randn(3,256,256))
local tnt = require 'torchnet'
local batchSize = 256

local function getIterator()
   return tnt.ParallelDatasetIterator{
      nthread = 4,
      init = function()
         require 'torchnet'
         require 'image'
         imtransform = M
      end,
      closure = function()
         local list = tnt.ListDataset{
            list = torch.range(1,batchSize*500):long(),
            load = function(x)
               return {
                  input = image.load('temp.jpg'):float(),
                  target = torch.LongTensor{x},
               }
            end,
         }:transform{
             input = tnt.transform.compose{
                      imtransform.Scale(256),
                      imtransform.RandomCrop(224),
                      imtransform.ColorNormalize(),
                      imtransform.HorizontalFlip(0.5),
                   }
         }:batch(batchSize,'skip-last')
         return list
      end,
  }
end
local iter = getIterator()
timer = torch.Timer()
timer:reset()
for x in iter() do
  print(timer:time().real)
end
print(timer:time().real)