Sample Classification Code of CIFAR-10 in Torch

Sample Classification Code of CIFAR-10 in Torch

from: http://torch.ch/blog/2015/07/30/cifar.html

require 'xlua'
require 'optim'
require 'nn'
require 'image'
local c = require 'trepl.colorize'

opt = lapp[[
   -s,--save                  (default "logs")      subdirectory to save logs
   -b,--batchSize             (default 128)          batch size
   -r,--learningRate          (default 1)        learning rate
   --learningRateDecay        (default 1e-7)      learning rate decay
   --weightDecay              (default 0.0005)      weightDecay
   -m,--momentum              (default 0.9)         momentum
   --epoch_step               (default 25)          epoch step
   --model                    (default vgg_bn_drop)     model name
   --max_epoch                (default 300)           maximum number of iterations
   --backend                  (default nn)            backend
   --type                     (default cuda)          cuda/float/cl
]]

print(opt)

do -- data augmentation module
  local BatchFlip,parent = torch.class('nn.BatchFlip', 'nn.Module')

  function BatchFlip:__init()
    parent.__init(self)
    self.train = true
  end

  function BatchFlip:updateOutput(input)
    if self.train then
      local bs = input:size()
      local flip_mask = torch.randperm(bs):le(bs/)
      for i=,input:size() do
        if flip_mask[i] ==  then image.hflip(input[i], input[i]) end
      end
    end
    self.output:set(input)
    return self.output
  end
end

local function cast(t)
   if opt.type == 'cuda' then
      require 'cunn'
      return t:cuda()
   elseif opt.type == 'float' then
      return t:float()
   elseif opt.type == 'cl' then
      require 'clnn'
      return t:cl()
   else
      error('Unknown type '..opt.type)
   end
end

print(c.blue '==>' ..' configuring model')
local model = nn.Sequential()
model:add(nn.BatchFlip():float())
model:add(cast(nn.Copy('torch.FloatTensor', torch.type(cast(torch.Tensor())))))
model:add(cast(dofile('models/'..opt.model..'.lua')))
model:get().updateGradInput = function(input) return end

if opt.backend == 'cudnn' then
   require 'cudnn'
   cudnn.benchmark=true
   cudnn.convert(model:get(), cudnn)
end

print(model)

print(c.blue '==>' ..' loading data')

-------------------------------------------------------------------------------------------
----------------------------  Load the Train and Test data  -------------------------------
-------------------------------------------------------------------------------------------

local trsize =
local tesize =
-- load dataset
trainData = {
   data = torch.Tensor(, ),
   labels = torch.Tensor(),
   size = function() return trsize end
}
local trainData = trainData
for i = , do
   local subset = torch.load('cifar-10-batches-t7/data_batch_' .. (i+) .. '.t7', 'ascii')
   trainData.data[{ {i*+, (i+)*} }] = subset.data:t()
   trainData.labels[{ {i*+, (i+)*} }] = subset.labels
end
trainData.labels = trainData.labels + 

local subset = torch.load('cifar-10-batches-t7/test_batch.t7', 'ascii')
testData = {
   data = subset.data:t():double(),
   labels = subset.labels[]:double(),
   size = function() return tesize end
}
local testData = testData
testData.labels = testData.labels + 

-- resize dataset (if using small version)
trainData.data = trainData.data[{ {,trsize} }]
trainData.labels = trainData.labels[{ {,trsize} }]

testData.data = testData.data[{ {,tesize} }]
testData.labels = testData.labels[{ {,tesize} }]

-- reshape data
trainData.data = trainData.data:reshape(trsize,,,)
testData.data = testData.data:reshape(tesize,,,)

----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
--    preprocessing data (color space + normalization)
----------------------------------------------------------------------------------
----------------------------------------------------------------------------------
print '<trainer> preprocessing data (color space + normalization)'
collectgarbage()

-- preprocess trainSet
local normalization = nn.SpatialContrastiveNormalization(, image.gaussian1D())
for i = ,trainData:size() do
   xlua.progress(i, trainData:size())
   -- rgb -> yuv
   local rgb = trainData.data[i]
   local yuv = image.rgb2yuv(rgb)
   -- normalize y locally:
   yuv[] = normalization(yuv[{{}}])
   trainData.data[i] = yuv
end
-- normalize u globally:
local mean_u = trainData.data:select(,):mean()
local std_u = trainData.data:select(,):std()
trainData.data:select(,):add(-mean_u)
trainData.data:select(,):div(std_u)
-- normalize v globally:
local mean_v = trainData.data:select(,):mean()
local std_v = trainData.data:select(,):std()
trainData.data:select(,):add(-mean_v)
trainData.data:select(,):div(std_v)

trainData.mean_u = mean_u
trainData.std_u = std_u
trainData.mean_v = mean_v
trainData.std_v = std_v

-- preprocess testSet
for i = ,testData:size() do
  xlua.progress(i, testData:size())
   -- rgb -> yuv
   local rgb = testData.data[i]
   local yuv = image.rgb2yuv(rgb)
   -- normalize y locally:
   yuv[{}] = normalization(yuv[{{}}])
   testData.data[i] = yuv
end
-- normalize u globally:
testData.data:select(,):add(-mean_u)
testData.data:select(,):div(std_u)
-- normalize v globally:
testData.data:select(,):add(-mean_v)
testData.data:select(,):div(std_v)

----------------------------------------------------------------------------------
-----------------------------   END  ---------------------------------------------

trainData.data = trainData.data:float()
testData.data = testData.data:float()

confusion = optim.ConfusionMatrix()

print('Will save at '..opt.save)
paths.mkdir(opt.save)
testLogger = optim.Logger(paths.concat(opt.save, 'test.log'))
testLogger:setNames{'% mean class accuracy (train set)', '% mean class accuracy (test set)'}
testLogger.showPlot = false

parameters,gradParameters = model:getParameters()

print(c.blue'==>' ..' setting criterion')
criterion = cast(nn.CrossEntropyCriterion())

print(c.blue'==>' ..' configuring optimizer')
optimState = {
  learningRate = opt.learningRate,
  weightDecay = opt.weightDecay,
  momentum = opt.momentum,
  learningRateDecay = opt.learningRateDecay,
}

function train()
  model:training()
  epoch = epoch or 

  -- drop learning rate every "epoch_step" epochs
  if epoch % opt.epoch_step ==  then optimState.learningRate = optimState.learningRate/ end

  print(c.blue '==>'.." online epoch # " .. epoch .. ' [batchSize = ' .. opt.batchSize .. ']')

  local targets = cast(torch.FloatTensor(opt.batchSize))
  local indices = torch.randperm(trainData.data:size()):long():split(opt.batchSize)
  -- remove last element so that all the batches have equal size
  indices[#indices] = nil

  local tic = torch.tic()
  for t,v in ipairs(indices) do
    xlua.progress(t, #indices)

    local inputs = trainData.data:index(,v)
    targets:copy(trainData.labels:index(,v))

    local feval = function(x)
      if x ~= parameters then parameters:copy(x) end
      gradParameters:zero()

      local outputs = model:forward(inputs)
      local f = criterion:forward(outputs, targets)
      local df_do = criterion:backward(outputs, targets)
      model:backward(inputs, df_do)

      confusion:batchAdd(outputs, targets)

      return f,gradParameters
    end
    optim.sgd(feval, parameters, optimState)
  end

  confusion:updateValids()
  print(('Train accuracy: '..c.cyan'%.2f'..' %%\t time: %.2f s'):format(
        confusion.totalValid * , torch.toc(tic)))

  train_acc = confusion.totalValid * 

  confusion:zero()
  epoch = epoch +
end

function test()
  -- disable flips, dropouts and batch normalization
  model:evaluate()
  print(c.blue '==>'.." testing")
  local bs =
  for i=,testData.data:size(),bs do
    local outputs = model:forward(testData.data:narrow(,i,bs))
    confusion:batchAdd(outputs, testData.labels:narrow(,i,bs))
  end

  confusion:updateValids()
  print('Test accuracy:', confusion.totalValid * )

  if testLogger then
    paths.mkdir(opt.save)
    testLogger:add{train_acc, confusion.totalValid * }
    testLogger:style{'-','-'}
    testLogger:plot()

    if paths.filep(opt.save..'/test.log.eps') then
      local base64im
      do
        os.execute(('convert -density 200 %s/test.log.eps %s/test.png'):format(opt.save,opt.save))
        os.execute(('openssl base64 -in %s/test.png -out %s/test.base64'):format(opt.save,opt.save))
        local f = io.open(opt.save..'/test.base64')
        if f then base64im = f:read'*all' end
      end

      local file = io.open(opt.save..'/report.html','w')
      file:write(([[
      <!DOCTYPE html>
      <html>
      <body>
      <title>%s - %s</title>
      <img src="data:image/png;base64,%s">
      <h4>optimState:</h4>
      <table>
      ]]):format(opt.save,epoch,base64im))
      for k,v in pairs(optimState) do
        if torch.type(v) == 'number' then
          file:write('<tr><td>'..k..'</td><td>'..v..'</td></tr>\n')
        end
      end
      file:write'</table><pre>\n'
      file:write(tostring(confusion)..'\n')
      file:write(tostring(model)..'\n')
      file:write'</pre></body></html>'
      file:close()
    end
  end

  -- save model every 50 epochs
  if epoch %  ==  then
    local filename = paths.concat(opt.save, 'model.net')
    print('==> saving model to '..filename)
    torch.save(filename, model:get():clearState())
  end

  confusion:zero()
end

for i=,opt.max_epoch do
  train()
  test()
end

the original version code:

why they written like this ?

It can not run ...