train.lua: Pass on comments.

training/train.lua

@@ -194,33 +194,35 @@ function trainBatch(inputsThread, numPerClassThread)
   local numTrips = 0
   for i = 1,opt.peoplePerBatch do
     local n = numPerClass[i]
-    for j = 1,n-1 do --for every image in batch
+    for j = 1,n-1 do -- For every image in the batch.
       local aIdx = embStartIdx + j - 1
       local diff = embeddings - embeddings[{ {aIdx} }]:expandAs(embeddings)
-      local norms = diff:norm(2, 2):pow(2):squeeze()    --L2 norm have be squared
-      for pair = j,n-1 do --create all posible positive pairs
+      local norms = diff:norm(2, 2):pow(2):squeeze()
+      for pair = j, n-1 do -- For every possible positive pair.
         local pIdx = embStartIdx + pair
-        -- Select a semi-hard negative that has a distance
-        -- further away from the positive exemplar. Oxford-Face Idea
 
-        --choose random example which is in margin
         local fff = (embeddings[aIdx]-embeddings[pIdx]):norm(2)
-        local normsP = norms - torch.Tensor(embeddings:size(1)):fill(fff*fff)  --L2 norm have be squared
-        --clean the idx of same class by setting to them max value
+        local normsP = norms - torch.Tensor(embeddings:size(1)):fill(fff*fff)
+
+        -- Set the indices of the same class to the max so they are ignored.
         normsP[{{embStartIdx,embStartIdx +n-1}}] = normsP:max()
-        -- get indexes of example which are inside margin
+
+        -- Get indices of images within the margin.
         local in_margin = normsP:lt(opt.alpha)
         local allNeg = torch.find(in_margin, 1)
 
-        if table.getn(allNeg) ~= 0 then  --use only non-random triplets. Random triples (which are beyond margin) will just produce gradient = 0, so average gradient will decrease
+        -- Use only non-random triplets.
+        -- Random triples (which are beyond the margin) will just produce gradient = 0,
+        -- so the average gradient will decrease.
+        if table.getn(allNeg) ~= 0 then
           selNegIdx = allNeg[math.random (table.getn(allNeg))]
-          --get embeding of each example
+          -- Add the embeding of each example.
           table.insert(as_table,embeddings[aIdx])
           table.insert(ps_table,embeddings[pIdx])
           table.insert(ns_table,embeddings[selNegIdx])
-          -- get original idx of triplets
-          table.insert(triplet_idx,{aIdx,pIdx,selNegIdx})
-          -- increase number of times of using each example, need for averaging then
+          -- Add the original index of triplets.
+          table.insert(triplet_idx, {aIdx,pIdx,selNegIdx})
+          -- Increase the number of times of using each example.
           num_example_per_idx[aIdx] = num_example_per_idx[aIdx] + 1
           num_example_per_idx[pIdx] = num_example_per_idx[pIdx] + 1
           num_example_per_idx[selNegIdx] = num_example_per_idx[selNegIdx] + 1