modify similarity_net ce (#4126)

PaddleNLP/similarity_net/run_classifier.py

@@ -92,11 +92,6 @@ def train(conf_dict, args):
     """
     train processic
     """
-    if args.enable_ce:
-        SEED = 102
-        fluid.default_startup_program().random_seed = SEED
-        fluid.default_main_program().random_seed = SEED
-
     # loading vocabulary
     vocab = utils.load_vocab(args.vocab_path)
     # get vocab size
@@ -124,6 +119,12 @@ def train(conf_dict, args):
     startup_prog = fluid.Program()
     train_program = fluid.Program()
 
+    # used for continuous evaluation 
+    if args.enable_ce:
+        SEED = 102
+        startup_prog.random_seed = SEED
+        train_program.random_seed = SEED
+
     simnet_process = reader.SimNetProcessor(args, vocab)
     if args.task_mode == "pairwise":
         # Build network
@@ -219,11 +220,15 @@ def train(conf_dict, args):
     ce_info = []
     train_exe = exe
     #for epoch_id in range(args.epoch):
-    train_batch_data = fluid.io.batch(
-        fluid.io.shuffle(
-            get_train_examples, buf_size=10000),
-        args.batch_size,
-        drop_last=False)
+    # used for continuous evaluation
+    if args.enable_ce:
+        train_batch_data = fluid.io.batch(get_train_examples, args.batch_size, drop_last=False)
+    else:
+        train_batch_data = fluid.io.batch(
+            fluid.io.shuffle(
+               get_train_examples, buf_size=10000),
+            args.batch_size,
+            drop_last=False)
     train_pyreader.decorate_paddle_reader(train_batch_data)
     train_pyreader.start()
     exe.run(startup_prog)
@@ -295,14 +300,14 @@ def train(conf_dict, args):
                                 target_vars, exe,
                                 test_prog)
     logging.info("saving infer model in %s" % model_path)
-
+    # used for continuous evaluation
     if args.enable_ce:
         card_num = get_cards()
         ce_loss = 0
         ce_time = 0
         try:
-            ce_loss = ce_info[-2][0]
-            ce_time = ce_info[-2][1]
+            ce_loss = ce_info[-1][0]
+            ce_time = ce_info[-1][1]
         except:
             logging.info("ce info err!")
         print("kpis\teach_step_duration_%s_card%s\t%s" %