Merge pull request #263 from littletomatodonkey/dyg/opt_code

simplify optimizer code

ppcls/optimizer/optimizer.py

@@ -16,18 +16,124 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import paddle.fluid.optimizer as pfopt
-import paddle.fluid.regularizer as pfreg
+import sys
+
+import paddle.fluid as fluid
 
 __all__ = ['OptimizerBuilder']
 
 
+class L1Decay(object):
+    """
+    L1 Weight Decay Regularization, which encourages the weights to be sparse.
+
+    Args:
+        factor(float): regularization coeff. Default:0.0.
+    """
+
+    def __init__(self, factor=0.0):
+        super(L1Decay, self).__init__()
+        self.regularization_coeff = factor
+
+    def __call__(self):
+        reg = fluid.regularizer.L1Decay(
+            regularization_coeff=self.regularization_coeff)
+        return reg
+
+
+class L2Decay(object):
+    """
+    L2 Weight Decay Regularization, which encourages the weights to be sparse.
+
+    Args:
+        factor(float): regularization coeff. Default:0.0.
+    """
+
+    def __init__(self, factor=0.0):
+        super(L2Decay, self).__init__()
+        self.regularization_coeff = factor
+
+    def __call__(self):
+        reg = fluid.regularizer.L2Decay(
+            regularization_coeff=self.regularization_coeff)
+        return reg
+
+
+class Momentum(object):
+    """
+    Simple Momentum optimizer with velocity state.
+
+    Args:
+        learning_rate (float|Variable) - The learning rate used to update parameters.
+            Can be a float value or a Variable with one float value as data element.
+        momentum (float) - Momentum factor.
+        regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
+    """
+
+    def __init__(self,
+                 learning_rate,
+                 momentum,
+                 parameter_list=None,
+                 regularization=None,
+                 **args):
+        super(Momentum, self).__init__()
+        self.learning_rate = learning_rate
+        self.momentum = momentum
+        self.parameter_list = parameter_list
+        self.regularization = regularization
+
+    def __call__(self):
+        opt = fluid.optimizer.Momentum(
+            learning_rate=self.learning_rate,
+            momentum=self.momentum,
+            parameter_list=self.parameter_list,
+            regularization=self.regularization)
+        return opt
+
+
+class RMSProp(object):
+    """
+    Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method.
+
+    Args:
+        learning_rate (float|Variable) - The learning rate used to update parameters.
+            Can be a float value or a Variable with one float value as data element.
+        momentum (float) - Momentum factor.
+        rho (float) - rho value in equation.
+        epsilon (float) - avoid division by zero, default is 1e-6.
+        regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
+    """
+
+    def __init__(self,
+                 learning_rate,
+                 momentum,
+                 rho=0.95,
+                 epsilon=1e-6,
+                 parameter_list=None,
+                 regularization=None,
+                 **args):
+        super(RMSProp, self).__init__()
+        self.learning_rate = learning_rate
+        self.momentum = momentum
+        self.rho = rho
+        self.epsilon = epsilon
+        self.parameter_list = parameter_list
+        self.regularization = regularization
+
+    def __call__(self):
+        opt = fluid.optimizer.RMSProp(
+            learning_rate=self.learning_rate,
+            momentum=self.momentum,
+            rho=self.rho,
+            epsilon=self.epsilon,
+            parameter_list=self.parameter_list,
+            regularization=self.regularization)
+        return opt
+
+
 class OptimizerBuilder(object):
     """
-    Build optimizer with fluid api in fluid.layers.optimizer,
-    such as fluid.layers.optimizer.Momentum()
-    https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/optimizer_cn.html
-    https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/regularizer_cn.html
+    Build optimizer
 
     Args:
         function(str): optimizer name of learning rate
@@ -43,13 +149,15 @@ class OptimizerBuilder(object):
         self.params = params
         # create regularizer
         if regularizer is not None:
+            mod = sys.modules[__name__]
             reg_func = regularizer['function'] + 'Decay'
-            reg_factor = regularizer['factor']
-            reg = getattr(pfreg, reg_func)(reg_factor)
+            del regularizer['function']
+            reg = getattr(mod, reg_func)(**regularizer)()
             self.params['regularization'] = reg
 
     def __call__(self, learning_rate, parameter_list):
-        opt = getattr(pfopt, self.function)
+        mod = sys.modules[__name__]
+        opt = getattr(mod, self.function)
         return opt(learning_rate=learning_rate,
                    parameter_list=parameter_list,
-                   **self.params)
+                   **self.params)()

tools/program.py

@@ -276,7 +276,6 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
         ("lr", AverageMeter(
             'lr', 'f', need_avg=False)),
         ("batch_time", AverageMeter('elapse', '.3f')),
-        ('reader_time', AverageMeter('reader', '.3f')),
     ]
     if not use_mix:
         topk_name = 'top{}'.format(config.topk)
@@ -287,7 +286,6 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
 
     tic = time.time()
     for idx, batch in enumerate(dataloader()):
-        metric_list['reader_time'].update(time.time() - tic)
         batch_size = len(batch[0])
         feeds = create_feeds(batch, use_mix)
         fetchs = create_fetchs(feeds, net, config, mode)