diff --git a/python/paddle/fluid/contrib/slim/quantization/imperative/ptq.py b/python/paddle/fluid/contrib/slim/quantization/imperative/ptq.py
index b85a4b66375457da35c3147c8e0939b1f8e28230..3a536ab1d2037680bbdaca8d5e65df306f528fcc 100644
--- a/python/paddle/fluid/contrib/slim/quantization/imperative/ptq.py
+++ b/python/paddle/fluid/contrib/slim/quantization/imperative/ptq.py
@@ -46,8 +46,8 @@ class ImperativePTQ(object):
         Args:
             quant_config(PTQConfig): the config of post training quantization.
                 The config has weight_quantizer and activation_quantizer.
-                In default, the weight_quantizer and activation_quantizer are
-                AbsmaxQuantizer.
+                In default, the weight_quantizer is PerChannelAbsmaxQuantizer
+                and the activation_quantizer is KLQuantizer.
         """
         super(ImperativePTQ, self).__init__()
 
@@ -70,9 +70,9 @@ class ImperativePTQ(object):
             "The model must be the instance of paddle.nn.Layer."
 
         if not inplace:
-            new_model = copy.deepcopy(model)
+            model = copy.deepcopy(model)
 
-        for name, layer in new_model.named_sublayers():
+        for name, layer in model.named_sublayers():
             if PTQRegistry.is_supported_layer(layer) \
                 and utils.is_leaf_layer(layer) \
                 and not self._is_skip_layer(layer):
@@ -90,13 +90,13 @@ class ImperativePTQ(object):
                 layer._forward_post_hooks.move_to_end(
                     quant_hook_handle._hook_id, last=False)
 
-        return new_model
+        return model
 
     def save_quantized_model(self, model, path, input_spec=None, **config):
         """
         1. Convert the quantized model
         2. Call jit.save to save the inference model
-        3. Load and postprocess the  inference model.
+        3. Post process the inference model.
 
         Args:
             model (Layer): The model to be saved.
@@ -207,8 +207,19 @@ class ImperativePTQ(object):
         assert isinstance(model, paddle.nn.Layer), \
             "The input model must be the instance of paddle.nn.Layer."
 
+        total_num = 0
+        cur_num = 0
         for name, sub_layer in model.named_sublayers():
             if self._is_quant_layer(sub_layer):
+                total_num += 1
+
+        for name, sub_layer in model.named_sublayers():
+            if self._is_quant_layer(sub_layer):
+                cur_num += 1
+                if cur_num % 5 == 0:
+                    _logger.info("Process the %s / %s layer" %
+                                 (cur_num, total_num))
+
                 quant_config = sub_layer._quant_config
 
                 if quant_config.enable_in_act_quantizer:
diff --git a/python/paddle/fluid/contrib/slim/quantization/imperative/ptq_config.py b/python/paddle/fluid/contrib/slim/quantization/imperative/ptq_config.py
index 1d089b32181d0f90e2090fc11ed7caf1be410b29..384d2c704fd88e683a077a1154be5ba98feb0806 100644
--- a/python/paddle/fluid/contrib/slim/quantization/imperative/ptq_config.py
+++ b/python/paddle/fluid/contrib/slim/quantization/imperative/ptq_config.py
@@ -53,4 +53,4 @@ class PTQConfig(object):
         self.enable_in_act_quantizer = False
 
 
-default_ptq_config = PTQConfig(AbsmaxQuantizer(), AbsmaxQuantizer())
+default_ptq_config = PTQConfig(KLQuantizer(), PerChannelAbsmaxQuantizer())
diff --git a/python/paddle/fluid/contrib/slim/tests/test_imperative_ptq.py b/python/paddle/fluid/contrib/slim/tests/test_imperative_ptq.py
index 24ae75456a0140529d6954decabdb9a33b6f1041..575a91642a7e76546bb42ab4db7a0524a9f6b7e1 100644
--- a/python/paddle/fluid/contrib/slim/tests/test_imperative_ptq.py
+++ b/python/paddle/fluid/contrib/slim/tests/test_imperative_ptq.py
@@ -82,7 +82,8 @@ class TestImperativePTQ(unittest.TestCase):
         return data_cache_folder
 
     def set_vars(self):
-        self.ptq = ImperativePTQ(default_ptq_config)
+        config = PTQConfig(AbsmaxQuantizer(), AbsmaxQuantizer())
+        self.ptq = ImperativePTQ(config)
 
         self.batch_num = 10
         self.batch_size = 10