speed up loading pretrained word2vec

fluid/text_matching_on_quora/pretrained_word2vec.py

@@ -4,22 +4,33 @@ This Module provide pretrained word-embeddings
 
 from __future__ import print_function 
 import numpy as np
+import time, datetime
 
-def Glove840B_300D(filepath="data/glove.840B.300d.txt"):
+def Glove840B_300D(filepath, keys=None):
     """
     input: the "glove.840B.300d.txt" file path
     return: a dict, key: word (unicode), value: a numpy array with shape [300]
     """
+    if keys is not None: 
+        assert(isinstance(keys, set))
     print("loading word2vec from ", filepath)
+    print("please wait for a minute.")
+    start = time.time()
     word2vec = {}
+
     with open(filepath, "r") as f:
-        lines = f.readlines()
-        for line in lines:
+        for line in f:
             info = line.strip().split()
-            word, vector = info[0], info[1:]
+            # TODO: test python3
+            word = info[0].decode('utf-8')
+            if (keys is not None) and (word not in keys):
+                continue
+            vector = info[1:]
             assert(len(vector) == 300)
-            #TODO: test python3
-            word2vec[word.decode('utf-8')] = np.asarray(vector, dtype='float32')
+            word2vec[word] = np.asarray(vector, dtype='float32')
+
+    end = time.time()
+    print("Spent ", str(datetime.timedelta(seconds=end-start)), " on loading word2vec.")
     return word2vec
  
 if __name__ == '__main__':

fluid/text_matching_on_quora/train_and_evaluate.py

@@ -219,7 +219,8 @@ def main():
  
     # load pretrained_word_embedding
     if global_config.use_pretrained_word_embedding:
-        word2vec = Glove840B_300D(filepath=os.path.join(DATA_DIR, "glove.840B.300d.txt"))  
+        word2vec = Glove840B_300D(filepath=os.path.join(DATA_DIR, "glove.840B.300d.txt"),
+                                  keys=set(word_dict.keys()))
         pretrained_word_embedding = utils.get_pretrained_word_embedding(
                                         word2vec=word2vec,
                                         word2id=word_dict,