Fix luacheck warnings.

.travis.yml

@@ -15,7 +15,7 @@ install:
 
 script:
   - flake8 --ignore=E402,E501 .
-  - luacheck . --no-global
+  - luacheck . --no-global --no-self --exclude-files '*/torch-TripletEmbedding'
   - |
     docker run -v $PWD:/root/src/openface bamos/openface \
            /bin/bash -l -c "cd /root/src/openface; \

batch-represent/batch-represent.lua

@@ -24,8 +24,8 @@ function batchRepresent()
       local indexEnd = math.min(nImgs, indexStart + opt.batchSize - 1)
       local inputs, labels = dumpLoader:get(indexStart, indexEnd)
       local paths = {}
-      for i=indexStart,indexEnd do
-         table.insert(paths, ffi.string(dumpLoader.imagePath[i]:data()))
+      for j=indexStart,indexEnd do
+         table.insert(paths, ffi.string(dumpLoader.imagePath[j]:data()))
       end
       repBatch(paths, inputs, labels)
       if i % 5 == 0 then

batch-represent/dataset.lua

@@ -3,9 +3,7 @@
 require 'torch'
 torch.setdefaulttensortype('torch.FloatTensor')
 local ffi = require 'ffi'
-local class = require('pl.class')
 local dir = require 'pl.dir'
-local tablex = require 'pl.tablex'
 local argcheck = require 'argcheck'
 require 'sys'
 require 'xlua'
@@ -22,7 +20,7 @@ local initcheck = argcheck{
 ]],
    {check=function(paths)
        local out = true;
-       for k,v in ipairs(paths) do
+       for _,v in ipairs(paths) do
           if type(v) ~= 'string' then
              print('paths can only be of string input');
              out = false
@@ -104,9 +102,9 @@ function dataset:__init(...)
    -- loop over each paths folder, get list of unique class names,
    -- also store the directory paths per class
    -- for each class,
-   for k,path in ipairs(self.paths) do
+   for _,path in ipairs(self.paths) do
       local dirs = dir.getdirectories(path);
-      for k,dirpath in ipairs(dirs) do
+      for _,dirpath in ipairs(dirs) do
          local class = paths.basename(dirpath)
          local idx = tableFind(self.classes, class)
          if not idx then
@@ -160,9 +158,9 @@ function dataset:__init(...)
    local tmpfile = os.tmpname()
    local tmphandle = assert(io.open(tmpfile, 'w'))
    -- iterate over classes
-   for i, class in ipairs(self.classes) do
+   for i, _ in ipairs(self.classes) do
       -- iterate over classPaths
-      for j,path in ipairs(classPaths[i]) do
+      for _,path in ipairs(classPaths[i]) do
          local command = find .. ' "' .. path .. '" ' .. findOptions
             .. ' >>"' .. classFindFiles[i] .. '" \n'
          tmphandle:write(command)
@@ -173,8 +171,8 @@ function dataset:__init(...)
    os.execute('rm -f ' .. tmpfile)
 
    print('now combine all the files to a single large file')
-   local tmpfile = os.tmpname()
-   local tmphandle = assert(io.open(tmpfile, 'w'))
+   tmpfile = os.tmpname()
+   tmphandle = assert(io.open(tmpfile, 'w'))
    -- concat all finds to a single large file in the order of self.classes
    for i=1,#self.classes do
       local command = 'cat "' .. classFindFiles[i] .. '" >>' .. combinedFindList .. ' \n'
@@ -214,16 +212,16 @@ function dataset:__init(...)
    local runningIndex = 0
    for i=1,#self.classes do
       if self.verbose then xlua.progress(i, #(self.classes)) end
-      local length = tonumber(sys.fexecute(wc .. " -l '"
+      local clsLength = tonumber(sys.fexecute(wc .. " -l '"
                                               .. classFindFiles[i] .. "' |"
                                               .. cut .. " -f1 -d' '"))
-      if length == 0 then
+      if clsLength == 0 then
          error('Class has zero samples: ' .. self.classes[i])
       else
-         self.classList[i] = torch.linspace(runningIndex + 1, runningIndex + length, length):long()
-         self.imageClass[{{runningIndex + 1, runningIndex + length}}]:fill(i)
+         self.classList[i] = torch.linspace(runningIndex + 1, runningIndex + clsLength, clsLength):long()
+         self.imageClass[{{runningIndex + 1, runningIndex + clsLength}}]:fill(i)
       end
-      runningIndex = runningIndex + length
+      runningIndex = runningIndex + clsLength
    end
 
    --==========================================================================
@@ -253,7 +251,7 @@ function dataset:__init(...)
       -- split the classList into classListTrain and classListTest
       for i=1,#self.classes do
          local list = self.classList[i]
-         local count = self.classList[i]:size(1)
+         count = self.classList[i]:size(1)
          local splitidx = math.floor((count * self.split / 100) + 0.5) -- +round
          local perm = torch.randperm(count)
          self.classListTrain[i] = torch.LongTensor(splitidx)
@@ -375,7 +373,7 @@ function dataset:sample(quantity)
    quantity = quantity or 1
    local dataTable = {}
    local scalarTable = {}
-   for i=1,quantity do
+   for _=1,quantity do
       local class = torch.random(1, #self.classes)
       local out = self:getByClass(class)
       table.insert(dataTable, out)
@@ -396,7 +394,7 @@ function dataset:sampleTriplet(quantity)
    local scalarTable = {}
 
    -- Anchors
-   for i=1,quantity do
+   for _=1,quantity do
       local anchorClass = torch.random(1, #self.classes)
       table.insert(dataTable, self:getByClass(anchorClass))
       table.insert(scalarTable, anchorClass)
@@ -444,8 +442,8 @@ function dataset:samplePeople(peoplePerBatch, imagesPerPerson)
       local cls = classes[i]
       local n = numPerClass[i]
       local shuffle = torch.randperm(n)
-      for i=1,n do
-         imgNum = self.classListSample[cls][shuffle[i]]
+      for j=1,n do
+         imgNum = self.classListSample[cls][shuffle[j]]
          imgPath = ffi.string(torch.data(self.imagePath[imgNum]))
          data[dataIdx] = self:sampleHookTrain(imgPath)
          dataIdx = dataIdx + 1

batch-represent/opts.lua

@@ -1,5 +1,3 @@
-local lfs = require 'lfs'
-
 local M = { }
 
 -- http://stackoverflow.com/questions/6380820/get-containing-path-of-lua-file

models/openface/nn2.def.lua

@@ -22,7 +22,7 @@
 -- limitations under the License.
 
 
-function createModel(nGPU)
+function createModel()
    local net = nn.Sequential()
 
    net:add(nn.SpatialConvolutionMM(3, 64, 7, 7, 2, 2, 3, 3))

openface/openface_server.lua

@@ -62,7 +62,7 @@ while true do
    if imgPath and imgPath:len() ~= 0 then
       img[1] = image.load(imgPath, 3, byte)
       img[1] = image.scale(img[1], opt.imgDim, opt.imgDim)
-      local rep = nil
+      local rep
       if opt.cuda then
          imgCuda:copy(img)
          rep = net:forward(imgCuda):float()

training/data.lua

@@ -32,8 +32,8 @@ do -- start K datathreads (donkeys)
    else -- single threaded data loading. useful for debugging
       paths.dofile('donkey.lua')
       donkeys = {}
-      function donkeys.addjob(_, f1, f2) f2(f1()) end
-      function donkeys.synchronize(_) end
+      function donkeys:addjob(f1, f2) f2(f1()) end
+      function donkeys:synchronize() end
    end
 end