add scope default val None

paddleslim/quant/quanter.py

@@ -26,6 +26,7 @@ WEIGHT_QUANTIZATION_TYPES=['abs_max', 'channel_wise_abs_max']
 ACTIVATION_QUANTIZATION_TYPES=['abs_max','range_abs_max', 'moving_average_abs_max']
 VALID_DTYPES = ['int8']
 
+
 _quant_config_default = {
         # weight quantize type, default is 'abs_max'
         'weight_quantize_type': 'abs_max',
@@ -50,6 +51,7 @@ _quant_config_default = {
         'quant_weight_only': False
     }
 
+
 def _parse_configs(user_config):
     """
     check user configs is valid, and set default value if user not config.
@@ -107,8 +109,7 @@ def _parse_configs(user_config):
     return configs
 
 
-
-def quant_aware(program, scope, place, config, for_test=False):
+def quant_aware(program, place, config, scope=None, for_test=False):
     """
     add trainable quantization ops in program.
     Args:
@@ -151,7 +152,8 @@ def quant_aware(program, scope, place, config, for_test=False):
         quant_program = fluid.CompiledProgram(main_graph.graph)
     return quant_program
 
-def quant_post(program, scope, place, config):
+
+def quant_post(program, place, config, scope=None):
     """
     add quantization ops in program. the program returned is not trainable.
     Args:
@@ -162,26 +164,9 @@ def quant_post(program, scope, place, config):
         for_test: is for test program.
     Return:
         fluid.Program: the quantization program is not trainable.
-    """
-
-    scope = fluid.global_scope() if not scope else scope
-    assert isinstance(config, dict), "config must be dict"
-    assert 'weight_quantize_type' in config.keys(), 'weight_quantize_type must be configured'
-    assert 'activation_quantize_type' in config.keys(), 'activation_quantize_type must be configured'
+    """   
+    pass
 
-    config = _parse_configs(config)
-
-    main_graph = IrGraph(core.Graph(program.desc), for_test=True)
-
-    transform_pass = QuantizationTransformPass(
-        scope=scope, place=place,
-        activation_quantize_type=config['activation_quantize_type'],
-        weight_quantize_type=config['weight_quantize_type'])
-    transform_pass.apply(main_graph)
-
-
-    quant_program = main_graph.to_program()
-    return quant_program
 
 def convert(program, scope, place, config, save_int8=False):
     """