diff --git a/tests/st/quantization/mobilenetv2_quant/test_mobilenetv2_quant.py b/tests/st/quantization/mobilenetv2_quant/test_mobilenetv2_quant.py
index 1a9f0708e7..363ef83d80 100644
--- a/tests/st/quantization/mobilenetv2_quant/test_mobilenetv2_quant.py
+++ b/tests/st/quantization/mobilenetv2_quant/test_mobilenetv2_quant.py
@@ -55,10 +55,10 @@ config_ascend_quant = ed({
 dataset_path = "/home/workspace/mindspore_dataset/cifar-10-batches-bin/"
 
 
-@pytest.mark.level1
+@pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
+@pytest.mark.env_single
 def test_mobilenetv2_quant():
     set_seed(1)
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -111,9 +111,12 @@ def test_mobilenetv2_quant():
                 dataset_sink_mode=False)
     print("============== End Training ==============")
 
+    export_time_used = 700
+    train_time = monitor.step_mseconds
+    print('train_time_used:{}'.format(train_time))
+    assert train_time < export_time_used
     expect_avg_step_loss = 2.32
     avg_step_loss = np.mean(np.array(monitor.losses))
-
     print("average step loss:{}".format(avg_step_loss))
     assert avg_step_loss < expect_avg_step_loss
 
diff --git a/tests/st/quantization/mobilenetv2_quant/utils.py b/tests/st/quantization/mobilenetv2_quant/utils.py
index 7b3ab0c3b7..60bd08f13a 100644
--- a/tests/st/quantization/mobilenetv2_quant/utils.py
+++ b/tests/st/quantization/mobilenetv2_quant/utils.py
@@ -45,7 +45,7 @@ class Monitor(Callback):
         self.lr_init = lr_init
         self.lr_init_len = len(lr_init)
         self.step_threshold = step_threshold
-        self.step_mseconds = 0
+        self.step_mseconds = 50000
 
     def epoch_begin(self, run_context):
         self.losses = []
@@ -66,7 +66,8 @@ class Monitor(Callback):
 
     def step_end(self, run_context):
         cb_params = run_context.original_args()
-        self.step_mseconds = (time.time() - self.step_time) * 1000
+        step_mseconds = (time.time() - self.step_time) * 1000
+        self.step_mseconds = min(self.step_mseconds, step_mseconds)
         step_loss = cb_params.net_outputs
 
         if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):