tf2onnx converted model cannot go to onnx-experimental.py because of naming conflicting

Author: pengwa

tests/test_internals.py

@@ -92,10 +92,10 @@ def test_insert_node1(self):
         result = onnx_to_graphviz(g)
         expected = 'digraph { Placeholder__4 [op_type=Placeholder] ' \
                    'n1 [op_type=Abs] n7 [op_type=Abs] n2 [op_type=Abs] n3 [op_type=Abs] ' \
-                   'n4 [op_type=Add] n5 [op_type=Abs] graph_outputs_Identity__3 [op_type=Identity] ' \
+                   'n4 [op_type=Add] n5 [op_type=Abs] n5_graph_outputs_Identity__3 [op_type=Identity] ' \
                    'n6 [op_type=Identity] input -> n1 n1:0 -> n7 n7:0 -> n2 n1:0 -> n3 ' \
-                   'n2:0 -> n4 n3:0 -> n4 n4:0 -> n5 raw_output___2:0 -> graph_outputs_Identity__3 ' \
-                   'raw_output___2:0 -> n6 }'
+                   'n2:0 -> n4 n3:0 -> n4 n4:0 -> n5 n5_raw_output___2:0 -> n5_graph_outputs_Identity__3 ' \
+                   'n5_raw_output___2:0 -> n6 }'
         self.assertEqual(expected, result)
 
     def test_insert_node2(self):
@@ -107,9 +107,9 @@ def test_insert_node2(self):
         result = onnx_to_graphviz(g)
         expected = 'digraph { Placeholder__4 [op_type=Placeholder] n1 [op_type=Abs] n7 [op_type=Abs] ' \
                    'n3 [op_type=Abs] n2 [op_type=Abs] n4 [op_type=Add] n5 [op_type=Abs] ' \
-                   'graph_outputs_Identity__3 [op_type=Identity] n6 [op_type=Identity] ' \
+                   'n5_graph_outputs_Identity__3 [op_type=Identity] n6 [op_type=Identity] ' \
                    'input -> n1 n1:0 -> n7 n7:0 -> n3 n7:0 -> n2 n2:0 -> n4 n3:0 -> n4 ' \
-                   'n4:0 -> n5 raw_output___2:0 -> graph_outputs_Identity__3 raw_output___2:0 -> n6 }'
+                   'n4:0 -> n5 n5_raw_output___2:0 -> n5_graph_outputs_Identity__3 n5_raw_output___2:0 -> n6 }'
         self.assertEqual(expected, result)
 
     def test_remove_input(self):
@@ -122,9 +122,9 @@ def test_remove_input(self):
         result = onnx_to_graphviz(g)
         expected = 'digraph { Placeholder__4 [op_type=Placeholder] n1 [op_type=Abs] n3 [op_type=Abs] ' \
                    'n2 [op_type=Abs] n4 [op_type=Add] n5 [op_type=Abs] ' \
-                   'graph_outputs_Identity__3 [op_type=Identity] n6 [op_type=Identity] ' \
+                   'n5_graph_outputs_Identity__3 [op_type=Identity] n6 [op_type=Identity] ' \
                    'input -> n1 n1:0 -> n3 n1:0 -> n2 n2:0 -> n4 n4:0 -> n5 ' \
-                   'raw_output___2:0 -> graph_outputs_Identity__3 raw_output___2:0 -> n6 }'
+                   'n5_raw_output___2:0 -> n5_graph_outputs_Identity__3 n5_raw_output___2:0 -> n6 }'
         self.assertEqual(expected, result)
 
     def test_rewrite_subgraph(self):
@@ -150,9 +150,9 @@ def test_rewrite_subgraph(self):
         result = onnx_to_graphviz(g)
         expected = 'digraph { Placeholder__4 [op_type=Placeholder] n1 [op_type=Abs] ' \
                    'n3 [op_type=Abs] n2 [op_type=Abs] ReplacedOp__5 [op_type=Sub] ' \
-                   'graph_outputs_Identity__3 [op_type=Identity] n6 [op_type=Identity] ' \
+                   'n5_graph_outputs_Identity__3 [op_type=Identity] n6 [op_type=Identity] ' \
                    'input -> n1 n1:0 -> n3 n1:0 -> n2 n2:0 -> ReplacedOp__5 ' \
-                   'n3:0 -> ReplacedOp__5 ReplacedOp__5:0 -> graph_outputs_Identity__3 ' \
+                   'n3:0 -> ReplacedOp__5 ReplacedOp__5:0 -> n5_graph_outputs_Identity__3 ' \
                    'ReplacedOp__5:0 -> n6 }'
         self.assertEqual(expected, result)
 

tf2onnx/graph.py

@@ -340,7 +340,7 @@ def __init__(self, nodes, output_shapes=None, dtypes=None, target=None, opset=No
         # add identity node after each output, in case it is renamed during conversion.
         for o in self.outputs:
             n = self.get_node_by_output_in_current_graph(o)
-            new_output_name = port_name(utils.make_name("raw_output_"))
+            new_output_name = port_name(n.name + "_" + utils.make_name("raw_output_"))
             n_shapes = n.output_shapes
             n_dtypes = n.output_dtypes
             body_graphs = n.graph.contained_graphs.pop(n.name, None)
@@ -356,7 +356,7 @@ def __init__(self, nodes, output_shapes=None, dtypes=None, target=None, opset=No
                     new_node.set_body_graph_as_attr(attr_name, body_graph)
 
             self.replace_all_inputs(self.get_nodes(), o, new_output_name)
-            self.make_node("Identity", [new_output_name], outputs=[o], op_name_scope="graph_outputs")
+            self.make_node("Identity", [new_output_name], outputs=[o], op_name_scope=n.name + "_" + "graph_outputs")
             self.copy_shape(new_output_name, o)
             self.copy_dtype(new_output_name, o)
 
@@ -1112,8 +1112,8 @@ def create_graph_from_onnx_graph(graph_proto):
             output_names.append(n.name)
 
         g = Graph(nodes_to_append, output_shapes, output_dtypes, None, None, None, output_names)
-        GraphUtil._parse_graph_initializer(g, graph_proto)
-        GraphUtil._parse_graph_input(g, graph_proto)
+        const_nodes = GraphUtil._parse_graph_initializer(g, graph_proto)
+        GraphUtil._parse_graph_input(g, graph_proto, [n.name for n in const_nodes])
 
         for n in g.get_nodes():
             for attr_name, attr_val in n.attr.items():
@@ -1164,10 +1164,11 @@ def _parse_graph_initializer(g, graph_proto):
         return const_nodes
 
     @staticmethod
-    def _parse_graph_input(g, graph_proto):
+    def _parse_graph_input(g, graph_proto, const_node_names):
         """Get graph inputs not defined as initializers and put into Graph object."""
         shapes, dtypes = GraphUtil._parse_shape_and_type_from_value_infos(graph_proto.input)
         for name in shapes:
             shape = shapes[name]
             dtype = dtypes[name]
-            g.add_graph_input(name, dtype, shape)
+            if name not in const_node_names:
+                g.add_graph_input(name, dtype, shape)

tools/onnx-experiments.py

@@ -81,7 +81,7 @@ def rewrite_constant_fold(g, ops):
                     log.info("folding node type=%s, name=%s", op.type, op.name)
                     if op.type == "Cast":
                         dst = op.get_attr_int("to")
-                        np_type = dst
+                        np_type = tf2onnx.utils.map_onnx_to_numpy_type(dst)
                         val = np.cast[np_type](*inputs)
                     elif op.type == "Transpose":
                         perm = op.get_attr("perm").ints
@@ -113,8 +113,9 @@ def rewrite_constant_fold(g, ops):
                     if consumers:
                         for consumer in consumers:
                             g.replace_input(consumer, old_output_name, new_output_name)
-                    for node in op.inputs:
-                        g.remove_node(node.name)
+                    for i, node in zip(op.input, op.inputs):
+                        if len(g.find_output_consumers(i)) == 1:
+                            g.remove_node(node.name)
                     keep_looking = True
                 except Exception as ex:  # pylint: disable=broad-except
                     tb = traceback.format_exc()