onnx_shape_inference support python2, delete repeate OP:greater

x2paddle/decoder/onnx_shape_inference.py

@@ -267,9 +267,9 @@ class SymbolicShapeInference:
 
         if pending_nodes and self.verbose_ > 0:
             print('SymbolicShapeInference: orphaned nodes discarded: ')
-            print(
-                * [n.op_type + ': ' + n.output[0] for n in pending_nodes],
-                sep='\n')
+            for n in pending_nodes:
+                print(n.op_type + ': ' + n.output[0])
+
         if input_shapes is not None:
             for input_name, shape in input_shapes.items():
                 for idx in range(len(self.out_mp_.graph.input)):

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -492,16 +492,6 @@ class OpSet9():
         node.fluid_code.add_layer(
             'hard_shrink', inputs=val_x, output=node, param_attr=attr)
 
-    def Greater(self, node):
-        val_x = self.graph.get_input_node(node, idx=0, copy=True)
-        val_y = self.graph.get_input_node(node, idx=1, copy=True)
-        node.fluid_code.add_layer(
-            'greater_than',
-            inputs={'x': val_x,
-                    'y': val_y},
-            output=node,
-            param_attr=None)
-
     @print_mapping_info
     def Constant(self, node):
         val_output = self.graph.get_node(node.layer.output[0], copy=True)
@@ -1160,7 +1150,6 @@ class OpSet9():
                     'y': cast_condition},
             output=mul_val_x,
             param_attr=None)
-
         mul_val_y = val_y.layer_name + '_mul'
         node.fluid_code.add_layer(
             "elementwise_mul",