fix bert performance test case

tests/st/networks/models/bert/test_bert_tdt_lossscale.py

@@ -101,12 +101,8 @@ def me_de_train_dataset(sink_mode=False):
     type_cast_op = C.TypeCast(mstype.int32)
     new_repeat_count = repeat_count
     if sink_mode:
-        repeat_count = 30
         sink_size = 100
-        ori_dataaet_size = ds.get_dataset_size()
+        new_repeat_count = 3
-        new_size = sink_size * batch_size
-        ds.set_dataset_size(new_size)
-        new_repeat_count = int(repeat_count * ori_dataaet_size // ds.get_dataset_size())
     ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
     ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
     ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
@@ -264,7 +260,7 @@ def test_bert_performance():
     config = get_config(version=version, batch_size=batch_size)
     netwithloss = BertNetworkWithLoss(config, True)
 
-    lr = BertLearningRate(decay_steps=ds.get_dataset_size()*new_repeat_count,
+    lr = BertLearningRate(decay_steps=sink_size * new_repeat_count,
                           learning_rate=5e-5, end_learning_rate=1e-9,
                           power=10.0, warmup_steps=0)
     decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
@@ -302,7 +298,7 @@ def test_bert_performance():
             else:
                 logger.info("***************** BERT param name is 3 {}".format(name))
                 param.default_input = weight_variable(value.asnumpy().shape)
-    time_monitor_callback = TimeMonitor(ds.get_dataset_size())
+    time_monitor_callback = TimeMonitor(sink_size)
     model.train(new_repeat_count, ds, callbacks=[time_monitor_callback, callback],
                 dataset_sink_mode=True, sink_size=sink_size)