fix quant_aware for 1.6 (#152)

demo/quant/quant_aware/train.py

@@ -12,7 +12,7 @@ sys.path.append(sys.path[0] + "/../../../")
 sys.path.append(sys.path[0] + "/../../")
 from paddleslim.common import get_logger
 from paddleslim.analysis import flops
-from paddleslim.quant import quant_aware, quant_post, convert
+from paddleslim.quant import quant_aware, convert
 import models
 from utility import add_arguments, print_arguments
 

paddleslim/quant/quanter.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import copy
+import logging
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.framework import IrGraph
@@ -22,6 +23,9 @@ from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
 from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
 from paddle.fluid import core
 
+from ..common import get_logger
+_logger = get_logger(__name__, level=logging.INFO)
+
 WEIGHT_QUANTIZATION_TYPES = [
     'abs_max', 'channel_wise_abs_max', 'range_abs_max',
     'moving_average_abs_max'
@@ -40,10 +44,8 @@ _quant_config_default = {
     'weight_bits': 8,
     # activation quantize bit num, default is 8
     'activation_bits': 8,
-    # ops of name_scope in not_quant_pattern list, will not be quantized
-    'not_quant_pattern': ['skip_quant'],
-    # ops of type in quantize_op_types, will be quantized
-    'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
+    # ops of name_scope in not_quant_pattern , will not be quantized
+    'not_quant_pattern': 'skip_quant',
     # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
     'dtype': 'int8',
     # window size for 'range_abs_max' quantization. defaulf is 10000
@@ -84,12 +86,9 @@ def _parse_configs(user_config):
     assert (configs['activation_bits'] >= 1 and configs['activation_bits'] <= 16), \
         "activation_bits should be between 1 and 16."
 
-    assert isinstance(configs['not_quant_pattern'], list), \
+    assert isinstance(configs['not_quant_pattern'], str), \
         "not_quant_pattern must be a list"
 
-    assert isinstance(configs['quantize_op_types'], list), \
-        "quantize_op_types must be a list"
-
     assert isinstance(configs['dtype'], str), \
         "dtype must be a str."
 
@@ -102,11 +101,6 @@ def _parse_configs(user_config):
     assert isinstance(configs['moving_rate'], float), \
         "moving_rate must be float value, The decay coefficient of moving average, default is 0.9."
 
-    assert isinstance(configs['quant_weight_only'], bool), \
-        "quant_weight_only must be bool value, if set quant_weight_only True, " \
-        "then only quantize parameters of layers which need to be quantized, " \
-        " and activations will not be quantized."
-
     return configs
 
 
@@ -142,7 +136,6 @@ def quant_aware(program, place, config=None, scope=None, for_test=False):
         weight_quantize_type=config['weight_quantize_type'],
         window_size=config['window_size'],
         moving_rate=config['moving_rate'],
-        quantizable_op_type=config['quantize_op_types'],
         skip_pattern=config['not_quant_pattern'])
 
     transform_pass.apply(main_graph)
@@ -185,6 +178,8 @@ def convert(program, place, config=None, scope=None, save_int8=False):
     freeze_pass = QuantizationFreezePass(
         scope=scope,
         place=place,
+        weight_bits=config['weight_bits'],
+        activation_bits=config['activation_bits'],
         weight_quantize_type=config['weight_quantize_type'])
     freeze_pass.apply(test_graph)
     freezed_program = test_graph.to_program()