add L2SP strategy

paddlehub/finetune/strategy.py

@@ -22,6 +22,7 @@ import multiprocessing
 import paddle.fluid as fluid
 
 from paddlehub.finetune.optimization import adam_weight_decay_optimization
+from paddlehub.finetune.regularizer import L2SPDecayRegularizer
 
 
 def get_pretrained_parameter(main_program, start_program):
@@ -42,8 +43,6 @@ class DefaultStrategy(object):
     def __init__(self, learning_rate=1e-4, optimizer_name="adam"):
         self.learning_rate = learning_rate
         self._optimizer_name = optimizer_name
-
-    def execute(self, loss):
         if self._optimizer_name.lower() == "sgd":
             self.optimizer = fluid.optimizer.SGD(
                 learning_rate=self.learning_rate)
@@ -75,6 +74,7 @@ class DefaultStrategy(object):
             self.optimizer = fluid.optimizer.Adam(
                 learning_rate=self.learning_rate)
 
+    def execute(self, loss):
         if self.optimizer is not None:
             self.optimizer.minimize(loss)
         else:
@@ -153,37 +153,35 @@ class DefaultFinetuneStrategy(DefaultStrategy):
         self.regularization_coeff = regularization_coeff
 
     def execute(self, loss):
-        if self._optimizer_name.lower() == "sgd":
-            self.optimizer = fluid.optimizer.SGD(
-                learning_rate=self.learning_rate)
-        elif self._optimizer_name.lower() == "adagrad":
-            self.optimizer = fluid.optimizer.Adagrad(
-                learning_rate=self.learning_rate)
-        elif self._optimizer_name.lower() == "adamax":
-            self.optimizer = fluid.optimizer.Adamax(
-                learning_rate=self.learning_rate)
-        elif self._optimizer_name.lower() == "decayedadagrad":
-            self.optimizer = fluid.optimizer.DecayedAdagrad(
-                learning_rate=self.learning_rate)
-        elif self._optimizer_name.lower() == "ftrl":
-            self.optimizer = fluid.optimizer.Ftrl(
-                learning_rate=self.learning_rate)
-        elif self._optimizer_name.lower() == "larsmomentum":
-            self.optimizer = fluid.optimizer.LarsMomentum(
-                learning_rate=self.learning_rate)
-        elif self._optimizer_name.lower() == "momentum":
-            self.optimizer = fluid.optimizer.Momentum(
-                learning_rate=self.learning_rate)
-        elif self._optimizer_name.lower() == "decayedadagrad":
-            self.optimizer = fluid.optimizer.DecayedAdagrad(
-                learning_rate=self.learning_rate)
-        elif self._optimizer_name.lower() == "rmsprop":
-            self.optimizer = fluid.optimizer.RMSPropOptimizer(
-                learning_rate=self.learning_rate)
+        # get pretrained parameters
+        program = loss.block.program
+        global_block = program.global_block()
+        pretrained_params = get_pretrained_parameter(
+            program, fluid.default_startup_program())
+
+        # set parameter attrs
+        for index, param in enumerate(pretrained_params):
+            param.regularizer = fluid.regularizer.L2Decay(
+                regularization_coeff=self.regularization_coeff)
+
+        if self.optimizer is not None:
+            self.optimizer.minimize(loss)
         else:
-            self.optimizer = fluid.optimizer.Adam(
-                learning_rate=self.learning_rate)
+            raise ValueError("DefaultFinetuneStrategy's optimizer is None")
+
+
+class L2SPFinetuneStrategy(DefaultStrategy):
+    def __init__(self,
+                 learning_rate=1e-4,
+                 optimizer_name="adam",
+                 regularization_coeff=1e-3):
+        super(L2SPFinetuneStrategy, self).__init__(
+            learning_rate=learning_rate, optimizer_name=optimizer_name)
+        self.learning_rate = learning_rate
+        self._optimizer_name = optimizer_name
+        self.regularization_coeff = regularization_coeff
 
+    def execute(self, loss):
         # get pretrained parameters
         program = loss.block.program
         global_block = program.global_block()
@@ -192,7 +190,7 @@ class DefaultFinetuneStrategy(DefaultStrategy):
 
         # set parameter attrs
         for index, param in enumerate(pretrained_params):
-            param.regularizer = fluid.regularizer.L2Decay(
+            param.regularizer = L2SPDecayRegularizer(
                 regularization_coeff=self.regularization_coeff)
 
         if self.optimizer is not None:

paddlehub/version.py

@@ -12,5 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ PaddleHub version string """
-hub_version = "0.4.6.beta"
+hub_version = "0.4.8.beta"
 module_proto_version = "1.0.0"