Measure functions

Author: Amartya Sanyal

CMakeLists.txt

@@ -84,6 +84,7 @@ SET(luasrc
   TotalDropout.lua
   VRClassReward.lua
   ReverseUnreverse.lua
+  measure.lua
   deprecated/SeqLSTMP.lua
   deprecated/SeqReverseSequence.lua
   deprecated/BiSequencerLM.lua

init.lua

@@ -27,7 +27,6 @@ paths.require 'librnn'
 unpack = unpack or table.unpack
 
 require('rnn.utils')
-
 -- extensions to existing nn.Module
 require('rnn.Module')
 require('rnn.Container')
@@ -112,6 +111,9 @@ require('rnn.SeqLSTMP')
 require('rnn.SeqReverseSequence')
 require('rnn.BiSequencerLM')
 
+
+require('rnn.measure')
+
 -- prevent likely name conflicts
 nn.rnn = rnn
 

measure.lua

@@ -0,0 +1,27 @@
+function nn.get_bleu(cand, ref, n)
+   n = n or 4
+   local smooth = 1
+   if type(cand) ~= 'table' then
+      cand = cand:totable()
+   end
+   if type(ref) ~= 'table' then
+      ref = ref:totable()
+   end
+   local res = nn.utils.get_ngram_prec(cand, ref, n)
+   local brevPen = math.exp(1-math.max(1, #ref/#cand))
+   local correct = 0
+   local total = 0
+   local bleu = 1
+   for i = 1, n do
+      if res[i][1] > 0 then
+         if res[i][2] == 0 then
+            smooth = smooth*0.5
+            res[i][2] = smooth
+         end
+         local prec = res[i][2]/res[i][1]
+         bleu = bleu * prec
+      end
+   end
+   bleu = bleu^(1/n)
+   return bleu*brevPen
+end

scripts/evaluate-rnnlm.lua

@@ -42,77 +42,6 @@ local validerr = xplog.valnceloss or xplog.valppl
 
 print(string.format("Error (epoch=%d): training=%f; validation=%f", xplog.epoch, trainerr[#trainerr], validerr[#validerr]))
 
-
-local function get_ngrams(sent, n, count)
-   local ngrams = {}
-   for beg = 1, #sent do
-      for  last= beg, math.min(beg+n-1, #sent) do
-         local ngram = table.concat(sent, ' ', beg, last)
-         local len = last-beg+1 -- keep track of ngram length
-         if not count then
-            table.insert(ngrams, ngram)
-         else
-            if ngrams[ngram] == nil then
-               ngrams[ngram] = {1, len}
-            else
-               ngrams[ngram][1] = ngrams[ngram][1] + 1
-            end
-         end
-      end
-   end
-   return ngrams
-end
-
-local function get_ngram_prec(cand, ref, n)
-   local results = {}
-   for i = 1, n do
-      results[i] = {0, 0}
-   end
-   local cand_ngrams = get_ngrams(cand, n, 1)
-   local ref_ngrams = get_ngrams(ref, n, 1)
-   for ngram, dist in pairs(cand_ngrams) do
-      local freq = dist[1]
-      local length = dist[2]
-      results[length][1] = results[length][1] + freq
-      local actual
-      if ref_ngrams[ngram] == nil then
-         actual = 0
-      else
-         actual = ref_ngrams[ngram][1]
-      end
-      results[length][2] = results[length][2] + math.min(actual, freq)
-   end
-   return results
-end
-
-function get_bleu(cand, ref, n)
-   n = n or 4
-   local smooth = 1
-   if type(cand) ~= 'table' then
-      cand = cand:totable()
-   end
-   if type(ref) ~= 'table' then
-      ref = ref:totable()
-   end
-   local res = get_ngram_prec(cand, ref, n)
-   local brevPen = math.exp(1-math.max(1, #ref/#cand))
-   local correct = 0
-   local total = 0
-   local bleu = 1
-   for i = 1, n do
-      if res[i][1] > 0 then
-         if res[i][2] == 0 then
-            smooth = smooth*0.5
-            res[i][2] = smooth
-         end
-         local prec = res[i][2]/res[i][1]
-         bleu = bleu * prec
-      end
-   end
-   bleu = bleu^(1/n)
-   return bleu*brevPen
-end
-
 if opt.dumpcsv then
    local csvfile = opt.xplogpath:match('([^/]+)[.]t7$')..'.csv'
    paths.mkdir('learningcurves')
@@ -220,7 +149,7 @@ else
       if opt.bleu then
          max_ind = torch.multinomial(torch.exp(outputs:view(targets:nElement(), -1)), 1):view(targets:size(1),targets:size(2))
             for batchIdx=1, targets:size(2) do
-               sum_bleu = sum_bleu + get_bleu(max_ind:select(2, batchIdx),
+               sum_bleu = sum_bleu + nn.get_bleu(max_ind:select(2, batchIdx),
                                               targets:select(2, batchIdx),
                                               opt.blueN)
                num_sent = num_sent + 1

utils.lua

@@ -290,4 +290,45 @@ function nn.utils.setZeroMask(modules, zeroMask, cuda)
    for i,module in ipairs(torch.type(modules) == 'table' and modules or {modules}) do
       module:setZeroMask(zeroMask)
    end
-end
+end
+function nn.utils.get_ngrams(sent, n, count)
+   local ngrams = {}
+   for beg = 1, #sent do
+      for  last= beg, math.min(beg+n-1, #sent) do
+         local ngram = table.concat(sent, ' ', beg, last)
+         local len = last-beg+1 -- keep track of ngram length
+         if not count then
+            table.insert(ngrams, ngram)
+         else
+            if ngrams[ngram] == nil then
+               ngrams[ngram] = {1, len}
+            else
+               ngrams[ngram][1] = ngrams[ngram][1] + 1
+            end
+         end
+      end
+   end
+   return ngrams
+end
+
+function nn.utils.get_ngram_prec(cand, ref, n)
+   local results = {}
+   for i = 1, n do
+      results[i] = {0, 0}
+   end
+   local cand_ngrams = nn.utils.get_ngrams(cand, n, 1)
+   local ref_ngrams = nn.utils.get_ngrams(ref, n, 1)
+   for ngram, dist in pairs(cand_ngrams) do
+      local freq = dist[1]
+      local length = dist[2]
+      results[length][1] = results[length][1] + freq
+      local actual
+      if ref_ngrams[ngram] == nil then
+         actual = 0
+      else
+         actual = ref_ngrams[ngram][1]
+      end
+      results[length][2] = results[length][2] + math.min(actual, freq)
+   end
+   return results
+end