enhanced m-test/lm_sample

1 files changed, 30 insertions, 15 deletions

diff --git a/nerv/examples/lmptb/m-tests/lm_sampler_test.lua b/nerv/examples/lmptb/m-tests/lm_sampler_test.lua
index 42a5787..0313d77 100644
--- a/nerv/examples/lmptb/m-tests/lm_sampler_test.lua
+++ b/nerv/examples/lmptb/m-tests/lm_sampler_test.lua
@@ -362,15 +362,12 @@ global_conf = {
 
 end
 
-lr_half = false --can not be local, to be set by loadstring
-start_iter = -1
-start_lr = nil
-ppl_last = 100000
 commands_str = "sampling" --"train:test"
 commands = {}
-test_iter = -1
---for testout(question)
-q_file = "/home/slhome/txh18/workspace/ptb/questionGen/gen/ptb.test.txt.q10rs1_Msss.adds"
+test_iter = -1 --obselete
+random_seed = 1
+sample_num = 10
+out_fn = nil
 
 if arg[2] ~= nil then
     nerv.printf("%s applying arg[2](%s)...\n", global_conf.sche_log_pre, arg[2])
@@ -407,16 +404,16 @@ end
 nerv.LMUtil.wait(2)
 
 nerv.printf("%s printing training scheduling options...\n", global_conf.sche_log_pre)
-nerv.printf("lr_half:\t%s\n", tostring(lr_half))
-nerv.printf("start_iter:\t%s\n", tostring(start_iter))
-nerv.printf("ppl_last:\t%s\n", tostring(ppl_last))
 nerv.printf("commands_str:\t%s\n", commands_str)
 nerv.printf("test_iter:\t%s\n", tostring(test_iter))
+nerv.printf("random_seed:\t%s\n", tostring(random_seed))
+nerv.printf("sample_num:\t%s\n", tostring(sample_num))
+nerv.printf("out_fn:\t%s\n", tostring(out_fn))
 nerv.printf("%s printing training scheduling end.\n", global_conf.sche_log_pre)
 nerv.LMUtil.wait(2)
 ------------------printing options end------------------------------
 
-math.randomseed(1)
+math.randomseed(random_seed)
 
 local vocab = nerv.LMVocab()
 global_conf["vocab"] = vocab
@@ -438,15 +435,33 @@ if commands["sampling"] == 1 then
     nerv.printf("===SAMPLE===\n") 
     global_conf.sche_log_pre = "[SCHEDULER SAMPLING]:" 
     local sampler = prepare_sampler(sm_conf)
-    for k = 1, 1 do
-        local res = sampler:lm_sample_rnn_dagL(10, {})
+    local out_fh = nil
+    if out_fn ~= nil then
+        out_fh = assert(io.open(out_fn, "w"))
+        nerv.printf("%s outputing samples to file \"%s\"...\n", global_conf.sche_log_pre, out_fn)
+    end
+    for k = 1, sample_num do
+        local res = sampler:lm_sample_rnn_dagL(1, {})
         for i = 1, #res do
+            if out_fh == nil then nerv.printf("lm_sampler_output_sample: ") end
             for j = 1, #res[i] do
-                nerv.printf("%s(%f) ", res[i][j].w, res[i][j].p)
+                if out_fh == nil then
+                    nerv.printf("%s %f ", res[i][j].w, res[i][j].p)
+                else
+                    out_fh:write(nerv.sprintf("%s %f ", res[i][j].w, res[i][j].p))
+                end
+            end
+            if out_fh == nil then
+                nerv.printf("\n")
+            else
+                out_fh:write(nerv.sprintf("\n"))
             end
-            nerv.printf("\n")
         end
+        if k % 100 == 0 and out_fh ~= nil then nerv.printf("%s %d sample done\n", global_conf.sche_log_pre, k) end
     end
+
+    if out_fh ~= nil then out_fh:close() end
+    nerv.printf("%s complete,bye\n", global_conf.sche_log_pre)
     --global_conf.dropout_rate = 0
     --LMTrainer.lm_process_file_rnn(global_conf, global_conf.test_fn, tnn, false) --false update!
 end --if commands["sampling"]