fix running err

paddleslim/quant/quanter.py

@@ -72,13 +72,13 @@ def _parse_configs(user_config):
     assert isinstance(configs['weight_bits'], int), \
         "weight_bits must be int value."
 
-    assert isinstance(configs['weight_bits'] >= 1 and configs['weight_bits'] <= 16), \
+    assert (configs['weight_bits'] >= 1 and configs['weight_bits'] <= 16), \
         "weight_bits should be between 1 and 16."
 
     assert isinstance(configs['activation_bits'], int), \
         "activation_bits must be int value."
 
-    assert isinstance(configs['activation_bits'] >= 1 and configs['activation_bits'] <= 16), \
+    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), \
@@ -90,7 +90,7 @@ def _parse_configs(user_config):
     assert isinstance(configs['dtype'], str), \
         "dtype must be a str."
 
-    assert isinstance(configs['dtype'] in VALID_DTYPES), \
+    assert (configs['dtype'] in VALID_DTYPES), \
         "dtype can only be " + " ".join(VALID_DTYPES)
 
     assert isinstance(configs['window_size'], int), \
@@ -140,7 +140,7 @@ def quant_aware(program, scope, place, config, for_test=False):
         window_size=config['window_size'],
         moving_rate=config['moving_rate'],
         quantizable_op_type=config['quantize_op_types'],
-        skip_pattern=''#not_quant_pattern
+        skip_pattern=config['not_quant_pattern']
     )
 
     transform_pass.apply(main_graph)

paddleslim/tests/quant_test/quanter_test.py

@@ -206,7 +206,7 @@ def train(args):
         # activation quantize bit num, default is 8
         'activation_bits': 8,
         # op of name_scope in not_quant_pattern list, will not quantized
-        'not_quant_pattern': ['skip_quant'],
+        'not_quant_pattern': ['skip_quant_dd'],
         # op of types in quantize_op_types, will quantized
         'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
         # data type after quantization, default is 'int8'
@@ -292,14 +292,16 @@ def train(args):
     #    According to the weight and activation quantization type, the graph will be added
     #    some fake quantize operators and fake dequantize operators.
     ############################################################################################################
-    train_prog = quant.quanter.quant_aware(train_prog, scope, place, quant_config, for_test = False)
-    test_prog = quant.quanter.quant_aware(test_prog, scope, place, quant_config, for_test=True)
-
-
-
     build_strategy = fluid.BuildStrategy()
     build_strategy.memory_optimize = False
     build_strategy.enable_inplace = False
+    build_strategy.fuse_all_reduce_ops = False
+    test_prog = quant.quanter.quant_aware(test_prog, scope, place, quant_config, for_test=True)
+    train_prog = quant.quanter.quant_aware(train_prog, scope, place, quant_config, for_test=False)
+    train_prog = train_prog.with_data_parallel(loss_name=train_cost.name, build_strategy=build_strategy)
+    #train_prog_binary = train_prog_binary.with_data_parallel(loss_name=train_cost.name)
+
+
     params = models.__dict__[args.model]().params
     for pass_id in range(params["num_epochs"]):
 

paddleslim/tests/quant_test/run_quant.sh

 #!/usr/bin/env bash
-source activate py27_paddle1.6
-
+source /home/wsz/anaconda2/bin/activate py27_paddle1.6
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
 #MobileNet v1:
-python quanter_test.py \
+nohup python quanter_test.py \
     --model=MobileNet \
     --pretrained_fp32_model='../../../../pretrain/MobileNetV1_pretrained/' \
     --use_gpu=True \
     --data_dir='/home/ssd8/wsz/tianfei01/traindata/imagenet/' \
-    --batch_size=256 \
+    --batch_size=2048 \
     --total_images=1281167 \
     --class_dim=1000 \
     --image_shape=3,224,224 \
@@ -16,7 +16,7 @@ python quanter_test.py \
     --num_epochs=20 \
     --lr=0.0001 \
     --act_quant_type=abs_max \
-    --wt_quant_type=abs_max
+    --wt_quant_type=abs_max 2>&1 &
 
 #ResNet50:
 #python quanter_test.py \