added one_sen_report to lm_process_file

1 files changed, 26 insertions, 8 deletions

diff --git a/nerv/examples/lmptb/lm_trainer.lua b/nerv/examples/lmptb/lm_trainer.lua
index 58d5bfc..3c7078e 100644
--- a/nerv/examples/lmptb/lm_trainer.lua
+++ b/nerv/examples/lmptb/lm_trainer.lua
@@ -22,15 +22,23 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
         p_conf = {}
     end
     local reader
+    local chunk_size, batch_size
     if p_conf.one_sen_report == true then --report log prob one by one sentence
         if do_train == true then
             nerv.warning("LMTrainer.lm_process_file_rnn: warning, one_sen_report is true while do_train is also true, strange")
         end
-        reader = nerv.LMSeqReader(global_conf, 1, global_conf.max_sen_len, global_conf.vocab)
+        nerv.printf("lm_process_file_rnn: one_sen report mode, set batch_size to 1 and chunk_size to max_sen_len(%d)\n", 
+                global_conf.max_sen_len)
+        batch_size = 1 
+        chunk_size = global_conf.max_sen_len
     else
-        reader = nerv.LMSeqReader(global_conf, global_conf.batch_size, global_conf.chunk_size, global_conf.vocab)
+        batch_size = global_conf.batch_size
+        chunk_size = global_conf.chunk_size
     end
+
+    reader = nerv.LMSeqReader(global_conf, batch_size, chunk_size, global_conf.vocab)
     reader:open_file(fn)
+
     local result = nerv.LMResult(global_conf, global_conf.vocab)
     result:init("rnn")
     if global_conf.dropout_rate ~= nil then
@@ -41,7 +49,7 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
     tnn:flush_all() --caution: will also flush the inputs from the reader!
 
     local next_log_wcn = global_conf.log_w_num
-    local neto_bakm = global_conf.mmat_type(global_conf.batch_size, 1) --space backup matrix for network output
+    local neto_bakm = global_conf.mmat_type(batch_size, 1) --space backup matrix for network output
 
     while (1) do
         global_conf.timer:tic('most_out_loop_lmprocessfile')
@@ -53,9 +61,9 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
             break 
         end
     
-        for t = 1, global_conf.chunk_size do
+        for t = 1, chunk_size do
             tnn.err_inputs_m[t][1]:fill(1)
-            for i = 1, global_conf.batch_size do
+            for i = 1, batch_size do
                 if bit.band(feeds.flags_now[t][i], nerv.TNN.FC.HAS_LABEL) == 0 then
                     tnn.err_inputs_m[t][1][i - 1][0] = 0
                 end
@@ -81,15 +89,26 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
         end
 
         global_conf.timer:tic('tnn_afterprocess')
-        for t = 1, global_conf.chunk_size, 1 do
+        local sen_logp = {}
+        for t = 1, chunk_size, 1 do
             tnn.outputs_m[t][1]:copy_toh(neto_bakm)
-            for i = 1, global_conf.batch_size, 1 do
+            for i = 1, batch_size, 1 do
                 if (feeds.labels_s[t][i] ~= global_conf.vocab.null_token) then
                     --result:add("rnn", feeds.labels_s[t][i], math.exp(tnn.outputs_m[t][1][i - 1][0]))
                     result:add("rnn", feeds.labels_s[t][i], math.exp(neto_bakm[i - 1][0]))
+                    if sen_logp[i] == nil then
+                        sen_logp[i] = 0
+                    end
+                    sen_logp[i] = sen_logp[i] + neto_bakm[i - 1][0]
                 end
             end            
         end
+        if p_conf.one_sen_report == true then
+            for i = 1, batch_size do
+                nerv.printf("LMTrainer.lm_process_file_rnn: one_sen_report, %f\n", sen_logp[i])    
+            end
+        end
+
         tnn:move_right_to_nextmb({0}) --only copy for time 0
         global_conf.timer:toc('tnn_afterprocess')
 
@@ -113,7 +132,6 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
         end
         ]]--
 
-
         collectgarbage("collect")                                              
 
         --break --debug