diff --git a/python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mobilenetv1.py b/python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mobilenetv1.py
index 37daeab1186299f23da8e90101ad12926ebde7f9..471798dec28c5c55baf66898da3afcc94e3fe2a4 100644
--- a/python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mobilenetv1.py
+++ b/python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mobilenetv1.py
@@ -209,7 +209,12 @@ class TestPostTrainingQuantization(unittest.TestCase):
             infer_program,
             feed_dict,
             fetch_targets,
-        ] = fluid.io.load_inference_model(model_path, exe)
+        ] = fluid.io.load_inference_model(
+            model_path,
+            exe,
+            model_filename="inference.pdmodel",
+            params_filename="inference.pdiparams",
+        )
         val_reader = paddle.batch(val(), batch_size)
         iterations = infer_iterations
 
@@ -224,16 +229,21 @@ class TestPostTrainingQuantization(unittest.TestCase):
             label = label.reshape([-1, 1])
 
             t1 = time.time()
-            _, acc1, _ = exe.run(
+            pred = exe.run(
                 infer_program,
-                feed={feed_dict[0]: image, feed_dict[1]: label},
+                feed={feed_dict[0]: image},
                 fetch_list=fetch_targets,
             )
             t2 = time.time()
             period = t2 - t1
             periods.append(period)
 
-            test_info.append(np.mean(acc1) * len(data))
+            pred = np.array(pred[0])
+            sort_array = pred.argsort(axis=1)
+            top_1_pred = sort_array[:, -1:][:, ::-1]
+            top_1 = np.mean(label == top_1_pred)
+
+            test_info.append(np.mean(top_1) * len(data))
             cnt += len(data)
 
             if (batch_id + 1) % 100 == 0:
@@ -277,6 +287,8 @@ class TestPostTrainingQuantization(unittest.TestCase):
             executor=exe,
             sample_generator=val_reader,
             model_dir=model_path,
+            model_filename="inference.pdmodel",
+            params_filename="inference.pdiparams",
             batch_size=batch_size,
             batch_nums=batch_nums,
             algo=algo,
@@ -288,7 +300,11 @@ class TestPostTrainingQuantization(unittest.TestCase):
             is_use_cache_file=is_use_cache_file,
         )
         ptq.quantize()
-        ptq.save_quantized_model(self.int8_model)
+        ptq.save_quantized_model(
+            self.int8_model,
+            model_filename="inference.pdmodel",
+            params_filename="inference.pdiparams",
+        )
 
     def run_test(
         self,
@@ -316,7 +332,7 @@ class TestPostTrainingQuantization(unittest.TestCase):
             )
         )
         (fp32_throughput, fp32_latency, fp32_acc1) = self.run_program(
-            os.path.join(model_cache_folder, "model"),
+            os.path.join(model_cache_folder, "MobileNetV1_infer"),
             batch_size,
             infer_iterations,
         )
@@ -327,7 +343,7 @@ class TestPostTrainingQuantization(unittest.TestCase):
             )
         )
         self.generate_quantized_model(
-            os.path.join(model_cache_folder, "model"),
+            os.path.join(model_cache_folder, "MobileNetV1_infer"),
             quantizable_op_type,
             batch_size,
             algo,
@@ -371,9 +387,9 @@ class TestPostTrainingKLForMobilenetv1(TestPostTrainingQuantization):
         algo = "KL"
         round_type = "round"
         data_urls = [
-            'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
+            'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar'
         ]
-        data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
+        data_md5s = ['5ee2b1775b11dc233079236cdc216c2e']
         quantizable_op_type = [
             "conv2d",
             "depthwise_conv2d",
@@ -405,9 +421,9 @@ class TestPostTrainingavgForMobilenetv1(TestPostTrainingQuantization):
         algo = "avg"
         round_type = "round"
         data_urls = [
-            'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
+            'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar'
         ]
-        data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
+        data_md5s = ['5ee2b1775b11dc233079236cdc216c2e']
         quantizable_op_type = [
             "conv2d",
             "depthwise_conv2d",
@@ -437,9 +453,9 @@ class TestPostTraininghistForMobilenetv1(TestPostTrainingQuantization):
         algo = "hist"
         round_type = "round"
         data_urls = [
-            'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
+            'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar'
         ]
-        data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
+        data_md5s = ['5ee2b1775b11dc233079236cdc216c2e']
         quantizable_op_type = [
             "conv2d",
             "depthwise_conv2d",
@@ -471,9 +487,9 @@ class TestPostTrainingAbsMaxForMobilenetv1(TestPostTrainingQuantization):
         algo = "abs_max"
         round_type = "round"
         data_urls = [
-            'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
+            'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar'
         ]
-        data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
+        data_md5s = ['5ee2b1775b11dc233079236cdc216c2e']
         quantizable_op_type = [
             "conv2d",
             "mul",
@@ -503,9 +519,9 @@ class TestPostTrainingAvgONNXFormatForMobilenetv1(TestPostTrainingQuantization):
         algo = "emd"
         round_type = "round"
         data_urls = [
-            'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
+            'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar'
         ]
-        data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
+        data_md5s = ['5ee2b1775b11dc233079236cdc216c2e']
         quantizable_op_type = [
             "conv2d",
             "depthwise_conv2d",