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 00a4e2c2aa49e03f7dfb41bca640624d6862f84c..41add5e8b8fb1eb41835873b086013cea83ad4a2 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
@@ -146,8 +146,6 @@ class TestPostTrainingQuantization(unittest.TestCase):
             os.system(cmd)
 
         self.batch_size = 1 if os.environ.get('DATASET') == 'full' else 50
-        self.sample_iterations = 50 if os.environ.get(
-            'DATASET') == 'full' else 2
         self.infer_iterations = 50000 if os.environ.get(
             'DATASET') == 'full' else 2
 
@@ -291,7 +289,6 @@ class TestPostTrainingQuantization(unittest.TestCase):
                  batch_nums=10):
         infer_iterations = self.infer_iterations
         batch_size = self.batch_size
-        sample_iterations = self.sample_iterations
 
         model_cache_folder = self.download_data(data_urls, data_md5s, model)
 
@@ -302,13 +299,12 @@ class TestPostTrainingQuantization(unittest.TestCase):
             infer_iterations)
 
         print("Start INT8 post training quantization for {0} on {1} images ...".
-              format(model, sample_iterations * batch_size))
+              format(model, batch_nums * batch_size))
         self.generate_quantized_model(os.path.join(model_cache_folder, "model"),
-                                      quantizable_op_type, batch_size,
-                                      sample_iterations, algo, round_type,
-                                      is_full_quantize, is_use_cache_file,
-                                      is_optimize_model, batch_nums,
-                                      onnx_format)
+                                      quantizable_op_type, batch_size, algo,
+                                      round_type, is_full_quantize,
+                                      is_use_cache_file, is_optimize_model,
+                                      batch_nums, onnx_format)
 
         print("Start INT8 inference for {0} on {1} images ...".format(
             model, infer_iterations * batch_size))
@@ -351,6 +347,7 @@ class TestPostTrainingKLForMobilenetv1(TestPostTrainingQuantization):
         is_use_cache_file = False
         is_optimize_model = True
         diff_threshold = 0.025
+        batch_nums = 3
         self.run_test(model, algo, round_type, data_urls, data_md5s,
                       quantizable_op_type, is_full_quantize, is_use_cache_file,
                       is_optimize_model, diff_threshold)