Fix make_const for strings (#1335)

Signed-off-by: Tom Wildenhain <[email protected]>

Author: TomWildenhain-Microsoft

tests/test_internals.py

@@ -108,6 +108,22 @@ def test_insert_node2(self):
                    'n5_raw_output___3:0 -> n6 n5_raw_output___3:0 -> n5_graph_outputs_Identity__4 }'
         self.assertEqual(expected, result)
 
+    def test_make_const_string(self):
+        graph_proto = self.sample_net()
+        g = GraphUtil.create_graph_from_onnx_graph(graph_proto)
+        arr1 = np.array("test", np.object)
+        arr2 = np.array([["A", "B"], ["C", "D"]], np.object)
+        arr3 = np.array(b"test", np.object)
+        arr4 = np.array([[b"A", b"B"], [b"C", b"D"]], np.object)
+        const1 = g.make_const("const1", arr1)
+        const2 = g.make_const("const2", arr2)
+        const3 = g.make_const("const3", arr3)
+        const4 = g.make_const("const4", arr4)
+        np.testing.assert_equal(const1.get_tensor_value(False), arr1)
+        np.testing.assert_equal(const2.get_tensor_value(False), arr2)
+        np.testing.assert_equal(const3.get_tensor_value(False), arr1)
+        np.testing.assert_equal(const4.get_tensor_value(False), arr2)
+
     def test_remove_input(self):
         graph_proto = self.sample_net()
         g = GraphUtil.create_graph_from_onnx_graph(graph_proto)

tf2onnx/graph.py

@@ -561,11 +561,13 @@ def make_const(self, name, np_val, skip_conversion=False, raw=True):
             skip_conversion: bool, indicate whether this created node would be mapped during conversion.
             raw: whether to store data at field of raw_data or the specific field according to its dtype
         """
-        if raw and np_val.dtype != np.object:
+        np_val_flat = np_val.flatten()
+        is_bytes = np_val.dtype == np.object and len(np_val_flat) > 0 and isinstance(np_val_flat[0], bytes)
+        if raw and not is_bytes:
             onnx_tensor = numpy_helper.from_array(np_val, name)
         else:
             onnx_tensor = helper.make_tensor(name, utils.map_numpy_to_onnx_dtype(np_val.dtype),
-                                             np_val.shape, np_val, raw=False)
+                                             np_val.shape, np_val_flat, raw=False)
         dtype = onnx_tensor.data_type
         node = self.make_node("Const", [], outputs=[name], name=name, attr={"value": onnx_tensor},
                               skip_conversion=skip_conversion, dtypes=[dtype], infer_shape_dtype=False)