diff --git a/test/quantization/test_post_training_quantization_mobilenetv1.py b/test/quantization/test_post_training_quantization_mobilenetv1.py
index 113b2cb066b91..4500f61ca13dc 100644
--- a/test/quantization/test_post_training_quantization_mobilenetv1.py
+++ b/test/quantization/test_post_training_quantization_mobilenetv1.py
@@ -25,7 +25,6 @@
 
 import paddle
 from paddle.dataset.common import download
-from paddle.io import Dataset
 from paddle.static.log_helper import get_logger
 from paddle.static.quantization import PostTrainingQuantization
 
@@ -117,33 +116,6 @@ def val(data_dir=DATA_DIR):
     return _reader_creator(file_list, 'val', shuffle=False, data_dir=data_dir)
 
 
-class ImageNetDataset(Dataset):
-    def __init__(self, data_dir=DATA_DIR, shuffle=False, need_label=False):
-        super().__init__()
-        self.need_label = need_label
-        self.data_dir = data_dir
-        val_file_list = os.path.join(data_dir, 'val_list.txt')
-        with open(val_file_list) as flist:
-            lines = [line.strip() for line in flist]
-            if shuffle:
-                np.random.shuffle(lines)
-            self.data = [line.split() for line in lines]
-
-    def __getitem__(self, index):
-        sample = self.data[index]
-        data_path = os.path.join(self.data_dir, sample[0])
-        data, label = process_image(
-            [data_path, sample[1]], mode='val', color_jitter=False, rotate=False
-        )
-        if self.need_label:
-            return data, np.array([label]).astype('int64')
-        else:
-            return data
-
-    def __len__(self):
-        return len(self.data)
-
-
 class TestPostTrainingQuantization(unittest.TestCase):
     def setUp(self):
         self.int8_download = 'int8/download'
@@ -295,7 +267,7 @@ def run_program(
         throughput = cnt / np.sum(periods)
         latency = np.average(periods)
         acc1 = np.sum(test_info) / cnt
-        return (throughput, latency, acc1, feed_dict)
+        return (throughput, latency, acc1)
 
     def generate_quantized_model(
         self,
@@ -312,7 +284,6 @@ def generate_quantized_model(
         batch_nums=1,
         onnx_format=False,
         deploy_backend=None,
-        feed_name="inputs",
     ):
         try:
             os.system("mkdir " + self.int8_model)
@@ -322,30 +293,11 @@ def generate_quantized_model(
 
         place = paddle.CPUPlace()
         exe = paddle.static.Executor(place)
-        image = paddle.static.data(
-            name=feed_name[0], shape=[None, 3, 224, 224], dtype='float32'
-        )
-        feed_list = [image]
-        if len(feed_name) == 2:
-            label = paddle.static.data(
-                name='label', shape=[None, 1], dtype='int64'
-            )
-            feed_list.append(label)
-
-        val_dataset = ImageNetDataset(need_label=len(feed_list) == 2)
-        data_loader = paddle.io.DataLoader(
-            val_dataset,
-            places=place,
-            feed_list=feed_list,
-            drop_last=False,
-            return_list=False,
-            batch_size=2,
-            shuffle=False,
-        )
+        val_reader = val()
 
         ptq = PostTrainingQuantization(
             executor=exe,
-            data_loader=data_loader,
+            sample_generator=val_reader,
             model_dir=model_path,
             model_filename=model_filename,
             params_filename=params_filename,
@@ -396,12 +348,7 @@ def run_test(
                 model, infer_iterations * batch_size
             )
         )
-        (
-            fp32_throughput,
-            fp32_latency,
-            fp32_acc1,
-            feed_name,
-        ) = self.run_program(
+        (fp32_throughput, fp32_latency, fp32_acc1) = self.run_program(
             model_path,
             model_filename,
             params_filename,
@@ -423,7 +370,6 @@ def run_test(
             batch_nums,
             onnx_format,
             deploy_backend,
-            feed_name,
         )
 
         _logger.info(
@@ -431,7 +377,7 @@ def run_test(
                 model, infer_iterations * batch_size
             )
         )
-        (int8_throughput, int8_latency, int8_acc1, _) = self.run_program(
+        (int8_throughput, int8_latency, int8_acc1) = self.run_program(
             self.int8_model,
             model_filename,
             params_filename,
@@ -475,7 +421,7 @@ def test_post_training_kl_mobilenetv1(self):
         is_use_cache_file = False
         is_optimize_model = True
         diff_threshold = 0.025
-        batch_nums = 2
+        batch_nums = 1
         self.run_test(
             model,
             'inference.pdmodel',
@@ -661,7 +607,7 @@ def test_post_training_onnx_format_mobilenetv1_tensorrt(self):
         is_optimize_model = False
         onnx_format = True
         diff_threshold = 0.05
-        batch_nums = 12
+        batch_nums = 2
         deploy_backend = "tensorrt"
         self.run_test(
             model,
@@ -704,7 +650,7 @@ def test_post_training_onnx_format_mobilenetv1_mkldnn(self):
         is_optimize_model = False
         onnx_format = True
         diff_threshold = 0.05
-        batch_nums = 12
+        batch_nums = 1
         deploy_backend = "mkldnn"
         self.run_test(
             model,
diff --git a/test/quantization/test_post_training_quantization_resnet50.py b/test/quantization/test_post_training_quantization_resnet50.py
index 895b2f170084d..ca87f17572a4c 100644
--- a/test/quantization/test_post_training_quantization_resnet50.py
+++ b/test/quantization/test_post_training_quantization_resnet50.py
@@ -113,7 +113,7 @@ def run_program(
         throughput = cnt / np.sum(periods)
         latency = np.average(periods)
         acc1 = np.sum(test_info) / cnt
-        return (throughput, latency, acc1, feed_dict)
+        return (throughput, latency, acc1)
 
 
 class TestPostTrainingForResnet50ONNXFormat(TestPostTrainingForResnet50):