top-k label ouput

@@ -34,6 +34,7 @@ from tensorflow import logging
 from tensorflow.python.lib.io import file_io
 import utils
 from collections import Counter
+import operator
 
 FLAGS = flags.FLAGS
 
@@ -81,7 +82,7 @@ if __name__ == "__main__":
       "the model graph and checkpoint will be bundled in this "
       "gzip tar. This file can be uploaded to Kaggle for the "
       "top 10 participants.")
-    flags.DEFINE_integer("top_k", 1, "How many predictions to output per video.")
+  flags.DEFINE_integer("top_k", 5, "How many predictions to output per video.")
 
   # Other flags.
   flags.DEFINE_integer("batch_size", 512,
@@ -260,6 +261,18 @@ def inference(reader, train_dir, data_pattern, out_file_location, batch_size,
 
     out_file.write(u"VideoId,LabelConfidencePairs\n".encode("utf8"))
     
+    #=========================================
+    #open vocab csv file and store to dictionary
+    #=========================================
+    voca_dict = {}
+    vocabs = open("./vocabulary.csv", 'r')
+    while True:
+      line = vocabs.readline()
+      if not line: break
+      vocab_dict_item = line.split(",")
+      if vocab_dict_item[0] != "Index":
+        voca_dict[vocab_dict_item[0]] = vocab_dict_item[3]
+    vocabs.close()
     try:
       while not coord.should_stop():
         video_id_batch_val, video_batch_val, num_frames_batch_val = sess.run(
@@ -308,7 +321,9 @@ def inference(reader, train_dir, data_pattern, out_file_location, batch_size,
         segment_id_list = []
         segment_classes = []
         cls_result_arr = []
+        cls_score_dict = {}
         out_file.seek(0, 0)
+        old_seg_name = '0000'
         for line in out_file:
           segment_id, preds = line.decode("utf8").split(",")
           if segment_id == "VideoId":
@@ -317,36 +332,48 @@ def inference(reader, train_dir, data_pattern, out_file_location, batch_size,
 
           preds = preds.split(" ")
           pred_cls_ids = [int(preds[idx]) for idx in range(0, len(preds), 2)]
-                    # =======================================
+          pred_cls_scores = [float(preds[idx]) for idx in range(1, len(preds), 2)]
+        #=======================================
           segment_id = str(segment_id.split(":")[0])
           if segment_id not in segment_id_list:
                 segment_id_list.append(str(segment_id))
                 segment_classes.append("")
 
           index = segment_id_list.index(segment_id)
-                    for classes in pred_cls_ids:
-                        segment_classes[index] = str(segment_classes[index]) + str(
-                            classes) + " "  # append classes from new segment
           
-                for segs, item in zip(segment_id_list, segment_classes):
+          if old_seg_name != segment_id:
+            cls_score_dict[segment_id] = {}
+            old_seg_name = segment_id
+          
+          for classes in range(0,len(pred_cls_ids)):#pred_cls_ids:
+                segment_classes[index] = str(segment_classes[index]) + str(pred_cls_ids[classes]) + " " #append classes from new segment
+                if pred_cls_ids[classes] in cls_score_dict[segment_id]:
+                  cls_score_dict[segment_id][pred_cls_ids[classes]] = cls_score_dict[segment_id][pred_cls_ids[classes]] + pred_cls_scores[classes]
+                else:
+                  cls_score_dict[segment_id][pred_cls_ids[classes]] = pred_cls_scores[classes]
+
+        for segs,item in zip(segment_id_list,segment_classes):
           print('====== R E C O R D ======')
           cls_arr = item.split(" ")[:-1]
           
-                    cls_arr = list(map(int, cls_arr))
-                    cls_arr = sorted(cls_arr)
+          cls_arr = list(map(int,cls_arr))      
+          cls_arr = sorted(cls_arr) #클래스별로 정렬
           
           result_string = ""
           
-                    temp = Counter(cls_arr)
-                    for item in temp:
-                        result_string = result_string + str(item) + ":" + str(temp[item]) + ","
+          temp = cls_score_dict[segs]
+          temp= sorted(temp.items(), key=operator.itemgetter(1), reverse=True) #밸류값 기준으로 정렬
+          demoninator = float(temp[0][1] + temp[1][1] + temp[2][1] + temp[3][1] + temp[4][1])
+          #for item in temp: 
+          for itemIndex in range(0, top_k):
+            result_string = result_string + str(voca_dict[str(temp[itemIndex][0])]) + ":" + format(temp[itemIndex][1]/demoninator,".3f") + ","
           
           cls_result_arr.append(result_string[:-1])
           logging.info(segs + " : " + result_string[:-1])
-                # =======================================
+        #=======================================
         final_out_file.write("vid_id,seg_classes\n")
         for seg_id, class_indcies in zip(segment_id_list, cls_result_arr):
-                    final_out_file.write("%s,%s\n" % (seg_id, str(class_indcies)))
+          final_out_file.write("%s,%s\n" %(seg_id, str(class_indcies)))
         final_out_file.close()
 
       out_file.close()
@@ -354,7 +381,6 @@ def inference(reader, train_dir, data_pattern, out_file_location, batch_size,
     coord.join(threads)
     sess.close()
 
-
 def main(unused_argv):
   logging.set_verbosity(tf.logging.INFO)
   if FLAGS.input_model_tgz: