add type check and default scope

python/paddle/fluid/inference_transpiler.py

@@ -13,26 +13,35 @@
 # limitations under the License.
 
 import numpy as np
-import os
+from framework import Program
-import shutil
+from executor import global_scope
 from . import core
 
 
 class InferenceTranspiler:
-    def transpile(self, program, scope, place):
+    def transpile(self, program, place, scope=None):
         '''
         Transpile the program. Support only fuse batch normalization now.
 
         :param program: program to transpile 
         :type program: Program
-        :param scope: inference scope 
-        :type scope: Scope
         :param place: inference place 
         :type place: Place
+        :param scope: inference scope 
+        :type scope: Scope or None
         '''
-        self.fuse_batch_norm(program, scope, place)
+        if not isinstance(program, Program):
-
+            raise TypeError("program should be as Program type")
-    def fuse_batch_norm(self, program, scope, place):
+        if not isinstance(place, core.CPUPlace) and not isinstance(
+                place, core.CUDAPlace):
+            raise TypeError("place should be as CPUPlace/CUDAPlace type")
+        if scope is None:
+            scope = global_scope()
+        if not isinstance(scope, core.Scope):
+            raise TypeError("scope should be as Scope type or None")
+        self.fuse_batch_norm(program, place, scope)
+
+    def fuse_batch_norm(self, program, place, scope):
         '''
         Transpile the program by fused batch normalization.
  
@@ -66,10 +75,10 @@ class InferenceTranspiler:
 
         :param program: program to transpile 
         :type program: Program
-        :param scope: inference scope 
-        :type scope: Scope
         :param place: inference place 
         :type place: Place
+        :param scope: inference scope 
+        :type scope: Scope
         '''
         self.scope = scope
         self.place = place

python/paddle/fluid/tests/book/test_image_classification.py

@@ -229,7 +229,7 @@ def infer(use_cuda, save_dirname=None):
         # Use inference_transpiler to speedup
         inference_transpiler_program = inference_program.clone()
         t = fluid.InferenceTranspiler()
-        t.transpile(inference_transpiler_program, inference_scope, place)
+        t.transpile(inference_transpiler_program, place)
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.