diff --git a/x2paddle/convert.py b/x2paddle/convert.py
index 901fbd7577969f114de70fc46bbba89add0408f3..c0c8fb98131b5f063b79334775ae63e27ab16c85 100644
--- a/x2paddle/convert.py
+++ b/x2paddle/convert.py
@@ -211,7 +211,10 @@ def main():
     try:
         import paddle
         v0, v1, v2 = paddle.__version__.split('.')
-        if int(v0) != 1 or int(v1) < 6:
+        print("paddle.__version__ = {}".format(paddle.__version__))
+        if v0 == '0' and v1 == '0' and v2 == '0':
+            print("[WARNING] You are use develop version of paddlepaddle")
+        elif int(v0) != 1 or int(v1) < 6:
             print("[ERROR] paddlepaddle>=1.6.0 is required")
             return
     except:
diff --git a/x2paddle/decoder/tf_decoder.py b/x2paddle/decoder/tf_decoder.py
index cf3b5ac502af00be0092c2270f2cb14b2731ebe9..97c5d486bc87feb807504c7bb9aa79c94e98beff 100644
--- a/x2paddle/decoder/tf_decoder.py
+++ b/x2paddle/decoder/tf_decoder.py
@@ -48,7 +48,10 @@ class TFGraphNode(GraphNode):
 
     @property
     def out_shapes(self):
-        values = self.layer.attr["_output_shapes"].list.shape
+        if self.layer_type == "OneShotIterator":
+            values = self.layer.attr["output_shapes"].list.shape
+        else:
+            values = self.layer.attr["_output_shapes"].list.shape
         out_shapes = list()
         for value in values:
             shape = [dim.size for dim in value.dim]
@@ -62,6 +65,8 @@ class TFGraphNode(GraphNode):
             dtype = self.layer.attr[k].type
             if dtype > 0:
                 break
+        if dtype == 0:
+            dtype = self.layer.attr['output_types'].list.type[0]
         if dtype not in self.dtype_map:
             raise Exception("Dtype[{}] not in dtype_map".format(dtype))
         return self.dtype_map[dtype]
@@ -226,7 +231,7 @@ class TFGraph(Graph):
     def _remove_identity_node(self):
         identity_ops = [
             'Identity', 'StopGradient', 'Switch', 'Merge',
-            'PlaceholderWithDefault'
+            'PlaceholderWithDefault', 'IteratorGetNext'
         ]
         identity_node = list()
         for node_name, node in self.node_map.items():
@@ -317,7 +322,7 @@ class TFDecoder(object):
         graph_def = cp.deepcopy(graph_def)
         input_map = dict()
         for layer in graph_def.node:
-            if layer.op != "Placeholder":
+            if layer.op != "Placeholder" and layer.op != "OneShotIterator":
                 continue
             graph_node = TFGraphNode(layer)
             dtype = graph_node.layer.attr['dtype'].type
@@ -335,6 +340,11 @@ class TFDecoder(object):
                 if shape.count(-1) > 1:
                     need_define_shape = 2
 
+            if need_define_shape == 1:
+                shape = graph_node.out_shapes[0]
+                if len(shape) > 0 and shape.count(-1) < 2:
+                    need_define_shape = 0
+
             if need_define_shape > 0:
                 shape = None
                 if graph_node.get_attr("shape"):
diff --git a/x2paddle/op_mapper/tf_op_mapper.py b/x2paddle/op_mapper/tf_op_mapper.py
index 0512c6edc520b44a2ea98546c21dad4cfb2de5cc..45fd2e8c570e43d0b77b559181bf363d09c80011 100644
--- a/x2paddle/op_mapper/tf_op_mapper.py
+++ b/x2paddle/op_mapper/tf_op_mapper.py
@@ -85,7 +85,7 @@ class TFOpMapper(OpMapper):
 
         not_placeholder = list()
         for name in self.graph.input_nodes:
-            if self.graph.get_node(name).layer_type != "Placeholder":
+            if self.graph.get_node(name).layer_type != "Placeholder" and self.graph.get_node(name).layer_type != "OneShotIterator":
                 not_placeholder.append(name)
         for name in not_placeholder:
             idx = self.graph.input_nodes.index(name)
@@ -287,6 +287,9 @@ class TFOpMapper(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
+    def OneShotIterator(self, node):
+        return self.Placeholder(node)
+
     def Const(self, node):
         shape = node.out_shapes[0]
         dtype = node.dtype
@@ -492,6 +495,9 @@ class TFOpMapper(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
+    def FusedBatchNormV3(self, node):
+        return self.FusedBatchNorm(node)
+
     def DepthwiseConv2dNative(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         kernel = self.graph.get_node(node.layer.input[1], copy=True)