speed up test label semantic roles (#10718)

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -182,12 +182,6 @@ def train(use_cuda, save_dirname=None, is_local=True):
     crf_decode = fluid.layers.crf_decoding(
         input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
-    chunk_evaluator = fluid.evaluator.ChunkEvaluator(
-        input=crf_decode,
-        label=target,
-        chunk_scheme="IOB",
-        num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))
-
     train_data = paddle.batch(
         paddle.reader.shuffle(
             paddle.dataset.conll05.test(), buf_size=8192),
@@ -203,7 +197,6 @@ def train(use_cuda, save_dirname=None, is_local=True):
 
     def train_loop(main_program):
         exe.run(fluid.default_startup_program())
-
         embedding_param = fluid.global_scope().find_var(
             embedding_name).get_tensor()
         embedding_param.set(
@@ -213,27 +206,19 @@ def train(use_cuda, save_dirname=None, is_local=True):
         start_time = time.time()
         batch_id = 0
         for pass_id in xrange(PASS_NUM):
-            chunk_evaluator.reset(exe)
             for data in train_data():
-                cost, precision, recall, f1_score = exe.run(
-                    main_program,
+                cost = exe.run(main_program,
                                feed=feeder.feed(data),
-                    fetch_list=[avg_cost] + chunk_evaluator.metrics)
-                pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
-                    exe)
+                               fetch_list=[avg_cost])
+                cost = cost[0]
 
                 if batch_id % 10 == 0:
-                    print("avg_cost:" + str(cost) + " precision:" + str(
-                        precision) + " recall:" + str(recall) + " f1_score:" +
-                          str(f1_score) + " pass_precision:" + str(
-                              pass_precision) + " pass_recall:" + str(
-                                  pass_recall) + " pass_f1_score:" + str(
-                                      pass_f1_score))
+                    print("avg_cost:" + str(cost))
                     if batch_id != 0:
                         print("second per batch: " + str((time.time(
                         ) - start_time) / batch_id))
                     # Set the threshold low to speed up the CI test
-                    if float(pass_precision) > 0.01:
+                    if float(cost) < 60.0:
                         if save_dirname is not None:
                             # TODO(liuyiqun): Change the target to crf_decode
                             fluid.io.save_inference_model(save_dirname, [