average_top1_confident.lua

require 'cudnn'
require 'inn'
require 'image'
require 'dcnn'

-- Loads the mapping from net outputs to human readable labels
function load_synset()
  local file = io.open 'synset_words.txt'
  local list = {}
  while true do
    local line = file:read()
    if not line then break end
    table.insert(list, string.sub(line,11))
  end
  return list
end

-- Converts an image from RGB to BGR format and subtracts mean
function preprocess(im, img_mean)
  -- rescale the image
  local im3 = image.scale(im,224,224,'bilinear')*255
  -- RGB2BGR
  local im4 = im3:clone()
  im4[{1,{},{}}] = im3[{3,{},{}}]
  im4[{3,{},{}}] = im3[{1,{},{}}]

  -- subtract imagenet mean
  return im4 - image.scale(img_mean, 224, 224, 'bilinear')
end

-- Lua implementation of PHP scandir function
function scandir(directory)
    local i, t, popen = 0, {}, io.popen
    for filename in popen('ls -a "'..directory..'"'):lines() do
        if filename:match "%.png$" then
            i = i + 1
            t[i] = filename
        end
    end
    return t, i
end


fileDir = '/usr/local/data/imagenet/train'
fileList, total_files = scandir( fileDir )




-- Setting up networks 
print '==> Loading network'

--net = torch.load('/home/yusheng/Workspace/DeepLearning/models/zeilerNet/zeilerNet.net')
net = torch.load('/usr/local/data/zeilerNet/zeilerNet.net')
net:cuda()
-- as we want to classify, let's disable dropouts by enabling evaluation mode
net:evaluate()

print '==> Loading synsets'
synset_words = load_synset()

print '==> Loading image and imagenet mean'
image_name = 'Goldfish3.jpg'
--image_name = 'lena.jpg'
--image_name='people2.jpg'
img_mean_name = 'ilsvrc_2012_mean.t7'

im = image.load(image_name)
img_mean = torch.load(img_mean_name).img_mean:transpose(3,1)

-- Have to resize and convert from RGB to BGR and subtract mean
print '==> Preprocessing'
I = preprocess(im, img_mean)

-- Replace pooling by dual pooling
unpooling_layers = dcnn:ReplaceDualPoolingModule(net:get(1))

_,classes = net:forward(I:cuda()):view(-1):float():sort(true)

for i=1,5 do
  print('predicted class '..tostring(i)..': ', synset_words[classes[i] ])
end