diff --git a/python/paddle/fluid/reader.py b/python/paddle/fluid/reader.py
index 06d74386b88a0b0466394cebd9076ea05bf470c5..ebe16a8bbbc31735fb203a6ac93c3e0f24ab3d35 100644
--- a/python/paddle/fluid/reader.py
+++ b/python/paddle/fluid/reader.py
@@ -97,6 +97,18 @@ class DataLoaderBase(object):
     def __next__(self):
         raise NotImplementedError()
 
+    @classmethod
+    def _check_input_array(cls, item):
+        arr = np.asarray(item)
+        if arr.dtype == np.object:
+            raise TypeError(
+                "\n\tFaild to convert input data to a regular ndarray :\n\t* Usually "
+                "this means the input data contains nested lists with different lengths. "
+                "\n\t* Check the reader function passed to 'decorate_batch_generator'"
+                " to locate the data causes this issue.\n\t* Please consider using "
+                "'fluid.create_lod_tensor' to convert it to a LoD-Tensor.")
+        return arr
+
 
 class DataLoader(object):
     """
@@ -807,17 +819,6 @@ class DygraphGeneratorLoader(DataLoaderBase):
             self._reset()
             six.reraise(*sys.exc_info())
 
-    @classmethod
-    def _check_input_array(cls, item):
-        arr = np.array(item)
-        if arr.dtype == np.object:
-            raise TypeError(
-                "\n\tFaild to convert input data to a regular ndarray :\n\t* Usually "
-                "this means the input data contains nested lists with different lengths. "
-                "\n\t* Check the reader function passed to 'decorate_batch_generator'"
-                " to locate the data causes this issue.\n\t* Please consider using "
-                "'fluid.create_lod_tensor' to convert it to a LoD-Tensor.")
-
     def _exit_thread_expectedly(self):
         self._thread_done_event.set()
         self._blocking_queue.close()
@@ -894,7 +895,7 @@ class DygraphGeneratorLoader(DataLoaderBase):
                 array = core.LoDTensorArray()
                 for item in sample:
                     if not isinstance(item, core.LoDTensor):
-                        self._check_input_array(item)
+                        item = self._check_input_array(item)
                         tmp = core.LoDTensor()
                         tmp.set(item, core.CPUPlace())
                         item = tmp
@@ -1115,19 +1116,6 @@ class GeneratorLoader(DataLoaderBase):
         assert not self._iterable, "reset() cannot be called when DataLoader is iterable"
         self._reset()
 
-    @classmethod
-    def _check_input_array(cls, item):
-        arr = np.array(item)
-        if arr.dtype == np.object:
-            raise TypeError((
-                "\n\tFaild to convert input data to a regular ndarray :\n\t* Usually "
-                "this means the input data contains nested lists with different lengths. "
-                "\n\t* Check the reader function passed to 'decorate_batch_generator'"
-                " to locate the data causes this issue.\n\t* Please consider using "
-                "'fluid.create_lod_tensor' to convert it to a LoD-Tensor."))
-
-        return arr
-
     def _start(self):
         def __thread_main__():
             try: