diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py
index 4065f7fe11258da58bd0e03888b2edaf82fc8543..ec3c67d89548f68d705a9b5de80e28597e9829da 100644
--- a/python/paddle/v2/inference.py
+++ b/python/paddle/v2/inference.py
@@ -21,30 +21,16 @@ class Inference(object):
         self.__gradient_machine__ = gm
         self.__data_types__ = topo.data_type()
 
-    def iter_infer(self, input=None, batch_size=None, reader=None,
-                   feeding=None):
+    def iter_infer(self, input, feeding=None):
         feeder = DataFeeder(self.__data_types__, feeding)
-        if reader is None:
-            assert input is not None and isinstance(input, collections.Iterable)
-            if not isinstance(input, collections.Iterable):
-                raise TypeError("When reader is None, input should be whole "
-                                "inference data and should be iterable")
-
-            if batch_size is None:
-                if not hasattr(input, '__len__'):
-                    raise ValueError("Should set batch size when input data "
-                                     "don't contain length.")
-                batch_size = len(input)
-
-            def __reader_impl__():
-                for each_sample in input:
-                    yield each_sample
-
-            reader = minibatch.batch(__reader_impl__, batch_size=batch_size)
-        else:
-            if input is not None:
-                raise ValueError("User should set either input or reader, "
-                                 "should not set them both.")
+        batch_size = len(input)
+
+        def __reader_impl__():
+            for each_sample in input:
+                yield each_sample
+
+        reader = minibatch.batch(__reader_impl__, batch_size=batch_size)
+
         self.__gradient_machine__.start()
         for data_batch in reader():
             yield self.__gradient_machine__.forwardTest(feeder(data_batch))