fix issue

deploy/slim/quantization/quant.py

@@ -53,6 +53,37 @@ from paddleslim.quant import quant_aware, convert
 from paddle.fluid.layer_helper import LayerHelper
 
 
+def pact(x):
+    """
+    Process a variable using the pact method you define
+    Args:
+        x(Tensor): Paddle Tensor, need to be preprocess before quantization
+    Returns:
+        The processed Tensor x.
+    """
+    helper = LayerHelper("pact", **locals())
+    dtype = 'float32'
+    init_thres = 20
+    u_param_attr = fluid.ParamAttr(
+        name=x.name + '_pact',
+        initializer=fluid.initializer.ConstantInitializer(value=init_thres),
+        regularizer=fluid.regularizer.L2Decay(0.0001),
+        learning_rate=1)
+    u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype)
+    x = fluid.layers.elementwise_sub(
+        x, fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param)))
+    x = fluid.layers.elementwise_add(
+        x, fluid.layers.relu(fluid.layers.elementwise_sub(-u_param, x)))
+    return x
+
+
+def get_optimizer():
+    """
+    Build a program using a model and an optimizer
+    """
+    return fluid.optimizer.AdamOptimizer(0.001)
+
+
 def main():
     train_build_outputs = program.build(
         config, train_program, startup_program, mode='train')
@@ -77,26 +108,6 @@ def main():
     exe = fluid.Executor(place)
     exe.run(startup_program)
 
-    def pact(x, name=None):
-        helper = LayerHelper("pact", **locals())
-        dtype = 'float32'
-        init_thres = 20
-        u_param_attr = fluid.ParamAttr(
-            name=x.name + '_pact',
-            initializer=fluid.initializer.ConstantInitializer(value=init_thres),
-            regularizer=fluid.regularizer.L2Decay(0.0001),
-            learning_rate=1)
-        u_param = helper.create_parameter(
-            attr=u_param_attr, shape=[1], dtype=dtype)
-        x = fluid.layers.elementwise_sub(
-            x, fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param)))
-        x = fluid.layers.elementwise_add(
-            x, fluid.layers.relu(fluid.layers.elementwise_sub(-u_param, x)))
-        return x
-
-    def get_optimizer():
-        return fluid.optimizer.AdamOptimizer(0.001)
-
     # 1. quantization configs
     quant_config = {
         # weight quantize type, default is 'channel_wise_abs_max'
@@ -151,14 +162,6 @@ def main():
     train_compile_program = program.create_multi_devices_program(
         quant_train_program, train_opt_loss_name, for_quant=True)
 
-    # dump mode structure
-    if config['Global']['debug']:
-        if train_alg_type == 'rec' and 'attention' in config['Global'][
-                'loss_type']:
-            logger.warning('Does not suport dump attention...')
-        else:
-            summary(quant_train_program)
-
     init_model(config, quant_train_program, exe)
 
     train_info_dict = {'compile_program':train_compile_program,\