add callZooFunc and change all callBigDlFunc to callZooFunc (intel-an…

…alytics#1793)

python/dllib/src/bigdl/dllib/estimator/estimator.py

@@ -14,7 +14,8 @@
 # limitations under the License.
 #
 
-from bigdl.util.common import JavaValue, callBigDlFunc
+from bigdl.util.common import JavaValue
+from zoo.common.utils import callZooFunc
 
 
 class Estimator(JavaValue):
@@ -24,9 +25,10 @@ class Estimator(JavaValue):
     Estimator wraps a model, and provide an uniform training, evaluation or prediction operation on
     both local host and distributed spark environment.
     """
+
     def __init__(self, model, optim_methods=None, model_dir=None, jvalue=None, bigdl_type="float"):
         self.bigdl_type = bigdl_type
-        self.value = jvalue if jvalue else callBigDlFunc(
+        self.value = jvalue if jvalue else callZooFunc(
             bigdl_type, self.jvm_class_constructor(), model, optim_methods, model_dir)
 
     def clear_gradient_clipping(self):
@@ -35,7 +37,7 @@ def clear_gradient_clipping(self):
         In order to take effect, it needs to be called before fit.
         :return:
         """
-        callBigDlFunc(self.bigdl_type, "clearGradientClipping")
+        callZooFunc(self.bigdl_type, "clearGradientClipping")
 
     def set_constant_gradient_clipping(self, min, max):
         """
@@ -45,7 +47,7 @@ def set_constant_gradient_clipping(self, min, max):
         :param max: The maximum value to clip by.
         :return:
         """
-        callBigDlFunc(self.bigdl_type, "setConstantGradientClipping", self.value, min, max)
+        callZooFunc(self.bigdl_type, "setConstantGradientClipping", self.value, min, max)
 
     def set_l2_norm_gradient_clipping(self, clip_norm):
         """
@@ -54,7 +56,7 @@ def set_l2_norm_gradient_clipping(self, clip_norm):
         :param clip_norm: Gradient L2-Norm threshold.
         :return:
         """
-        callBigDlFunc(self.bigdl_type, "setGradientClippingByL2Norm", self.value, clip_norm)
+        callZooFunc(self.bigdl_type, "setGradientClippingByL2Norm", self.value, clip_norm)
 
     def train(self, train_set, criterion, end_trigger=None, checkpoint_trigger=None,
               validation_set=None, validation_method=None, batch_size=32):
@@ -73,9 +75,9 @@ def train(self, train_set, criterion, end_trigger=None, checkpoint_trigger=None,
         :param batch_size:
         :return: Estimator
         """
-        callBigDlFunc(self.bigdl_type, "estimatorTrain", self.value, train_set,
-                      criterion, end_trigger, checkpoint_trigger, validation_set,
-                      validation_method, batch_size)
+        callZooFunc(self.bigdl_type, "estimatorTrain", self.value, train_set,
+                    criterion, end_trigger, checkpoint_trigger, validation_set,
+                    validation_method, batch_size)
 
     def train_imagefeature(self, train_set, criterion, end_trigger=None, checkpoint_trigger=None,
                            validation_set=None, validation_method=None, batch_size=32):
@@ -94,9 +96,9 @@ def train_imagefeature(self, train_set, criterion, end_trigger=None, checkpoint_
         :param batch_size: Batch size
         :return:
         """
-        callBigDlFunc(self.bigdl_type, "estimatorTrainImageFeature", self.value, train_set,
-                      criterion, end_trigger, checkpoint_trigger, validation_set,
-                      validation_method, batch_size)
+        callZooFunc(self.bigdl_type, "estimatorTrainImageFeature", self.value, train_set,
+                    criterion, end_trigger, checkpoint_trigger, validation_set,
+                    validation_method, batch_size)
 
     def evaluate(self, validation_set, validation_method, batch_size=32):
         """
@@ -106,8 +108,8 @@ def evaluate(self, validation_set, validation_method, batch_size=32):
         :param batch_size: batch size
         :return: validation results
         """
-        callBigDlFunc(self.bigdl_type, "estimatorEvaluate", self.value,
-                      validation_set, validation_method, batch_size)
+        callZooFunc(self.bigdl_type, "estimatorEvaluate", self.value,
+                    validation_set, validation_method, batch_size)
 
     def evaluate_imagefeature(self, validation_set, validation_method, batch_size=32):
         """
@@ -117,5 +119,5 @@ def evaluate_imagefeature(self, validation_set, validation_method, batch_size=32
         :param batch_size: batch size
         :return: validation results
         """
-        callBigDlFunc(self.bigdl_type, "estimatorEvaluateImageFeature", self.value,
-                      validation_set, validation_method, batch_size)
+        callZooFunc(self.bigdl_type, "estimatorEvaluateImageFeature", self.value,
+                    validation_set, validation_method, batch_size)