fix bug

fluid/PaddleRec/gru4rec/cluster_train.py

@@ -13,22 +13,26 @@ import net
 
 SEED = 102
 
+
 def parse_args():
     parser = argparse.ArgumentParser("gru4rec benchmark.")
     parser.add_argument(
-        '--train_dir', type=str, default='train_data', help='train file address')
-    parser.add_argument(
-        '--vocab_path', type=str, default='vocab.txt', help='vocab file address')
-    parser.add_argument(
-        '--is_local', type=int, default=1, help='whether local')
+        '--train_dir',
+        type=str,
+        default='train_data',
+        help='train file address')
     parser.add_argument(
-        '--hid_size', type=int, default=100, help='hid size')
+        '--vocab_path',
+        type=str,
+        default='vocab.txt',
+        help='vocab file address')
+    parser.add_argument('--is_local', type=int, default=1, help='whether local')
+    parser.add_argument('--hid_size', type=int, default=100, help='hid size')
     parser.add_argument(
         '--model_dir', type=str, default='model_recall20', help='model dir')
     parser.add_argument(
         '--batch_size', type=int, default=5, help='num of batch size')
-    parser.add_argument(
-        '--pass_num', type=int, default=10, help='num of epoch')
+    parser.add_argument('--pass_num', type=int, default=10, help='num of epoch')
     parser.add_argument(
         '--print_batch', type=int, default=10, help='num of print batch')
     parser.add_argument(
@@ -40,19 +44,33 @@ def parse_args():
     parser.add_argument(
         '--role', type=str, default='pserver', help='trainer or pserver')
     parser.add_argument(
-        '--endpoints', type=str, default='127.0.0.1:6000', help='The pserver endpoints, like: 127.0.0.1:6000, 127.0.0.1:6001')
-    parser.add_argument(
-        '--current_endpoint', type=str, default='127.0.0.1:6000', help='The current_endpoint')
-    parser.add_argument(
-        '--trainer_id', type=int, default=0, help='trainer id ,only trainer_id=0 save model')
-    parser.add_argument(
-        '--trainers', type=int, default=1, help='The num of trianers, (default: 1)')
+        '--endpoints',
+        type=str,
+        default='127.0.0.1:6000',
+        help='The pserver endpoints, like: 127.0.0.1:6000, 127.0.0.1:6001')
+    parser.add_argument(
+        '--current_endpoint',
+        type=str,
+        default='127.0.0.1:6000',
+        help='The current_endpoint')
+    parser.add_argument(
+        '--trainer_id',
+        type=int,
+        default=0,
+        help='trainer id ,only trainer_id=0 save model')
+    parser.add_argument(
+        '--trainers',
+        type=int,
+        default=1,
+        help='The num of trianers, (default: 1)')
     args = parser.parse_args()
     return args
 
+
 def get_cards(args):
     return args.num_devices
 
+
 def train():
     """ do training """
     args = parse_args()
@@ -67,12 +85,13 @@ def train():
         buffer_size=1000, word_freq_threshold=0, is_train=True)
 
     # Train program
-    src_wordseq, dst_wordseq, avg_cost, acc = net.network(vocab_size=vocab_size, hid_size=hid_size)
+    src_wordseq, dst_wordseq, avg_cost, acc = net.all_vocab_network(
+        vocab_size=vocab_size, hid_size=hid_size)
 
     # Optimization to minimize lost
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=args.base_lr)
     sgd_optimizer.minimize(avg_cost)
-    
+
     def train_loop(main_program):
         """ train network """
         pass_num = args.pass_num
@@ -97,9 +116,11 @@ def train():
                 lod_dst_wordseq = utils.to_lodtensor([dat[1] for dat in data],
                                                      place)
                 ret_avg_cost = exe.run(main_program,
-                                            feed={  "src_wordseq": lod_src_wordseq,
-                                                    "dst_wordseq": lod_dst_wordseq},
-                                            fetch_list=fetch_list)
+                                       feed={
+                                           "src_wordseq": lod_src_wordseq,
+                                           "dst_wordseq": lod_dst_wordseq
+                                       },
+                                       fetch_list=fetch_list)
                 avg_ppl = np.exp(ret_avg_cost[0])
                 newest_ppl = np.mean(avg_ppl)
                 if i % args.print_batch == 0:
@@ -113,7 +134,8 @@ def train():
             feed_var_names = ["src_wordseq", "dst_wordseq"]
             fetch_vars = [avg_cost, acc]
             if args.trainer_id == 0:
-                fluid.io.save_inference_model(save_dir, feed_var_names, fetch_vars, exe)
+                fluid.io.save_inference_model(save_dir, feed_var_names,
+                                              fetch_vars, exe)
                 print("model saved in %s" % save_dir)
         print("finish training")
 
@@ -123,7 +145,8 @@ def train():
     else:
         print("run distribute training")
         t = fluid.DistributeTranspiler()
-        t.transpile(args.trainer_id, pservers=args.endpoints, trainers=args.trainers)
+        t.transpile(
+            args.trainer_id, pservers=args.endpoints, trainers=args.trainers)
         if args.role == "pserver":
             print("run psever")
             pserver_prog = t.get_pserver_program(args.current_endpoint)
@@ -136,5 +159,6 @@ def train():
             print("run trainer")
             train_loop(t.get_trainer_program())
 
+
 if __name__ == "__main__":
     train()

fluid/PaddleRec/gru4rec/infer.py

@@ -11,23 +11,27 @@ import paddle
 
 import utils
 
+
 def parse_args():
     parser = argparse.ArgumentParser("gru4rec benchmark.")
     parser.add_argument(
         '--test_dir', type=str, default='test_data', help='test file address')
     parser.add_argument(
-        '--start_index', type=int, default='1', help='start index')  
+        '--start_index', type=int, default='1', help='start index')
+    parser.add_argument(
+        '--last_index', type=int, default='10', help='end index')
     parser.add_argument(
-        '--last_index', type=int, default='10', help='end index')  
+        '--model_dir', type=str, default='model_recall20', help='model dir')
     parser.add_argument(
-        '--model_dir', type=str, default='model_recall20', help='model dir')  
+        '--use_cuda', type=int, default='0', help='whether use cuda')
     parser.add_argument(
-        '--use_cuda', type=int, default='1', help='whether use cuda')  
+        '--batch_size', type=int, default='5', help='batch_size')
     parser.add_argument(
-        '--batch_size', type=int, default='5', help='batch_size')  
+        '--vocab_path', type=str, default='vocab.txt', help='vocab file')
     args = parser.parse_args()
     return args
 
+
 def infer(test_reader, use_cuda, model_path):
     """ inference function """
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
@@ -72,11 +76,16 @@ if __name__ == "__main__":
     test_dir = args.test_dir
     model_dir = args.model_dir
     batch_size = args.batch_size
+    vocab_path = args.vocab_path
     use_cuda = True if args.use_cuda else False
-    print("start index: ", start_index, " last_index:" ,last_index)
+    print("start index: ", start_index, " last_index:", last_index)
     vocab_size, test_reader = utils.prepare_data(
-        test_dir, "", batch_size=batch_size,
-        buffer_size=1000, word_freq_threshold=0, is_train=False)
+        test_dir,
+        vocab_path,
+        batch_size=batch_size,
+        buffer_size=1000,
+        word_freq_threshold=0,
+        is_train=False)
 
     for epoch in range(start_index, last_index + 1):
         epoch_path = model_dir + "/epoch_" + str(epoch)