support for AsString, StringToNumber

tests/test_backend.py

@@ -5541,6 +5541,36 @@ def func(x):
         x_val = np.array([1, 5, 2, 0, 3, 4], dtype=np.int64)
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 
+    @check_tf_min_version("2.1")
+    @skip_tflite("TFlite errors on some attributes")
+    @check_opset_min_version(9, "string")
+    def test_asstring(self):
+        def func(x):
+            op_ = tf.strings.as_string(x)
+            return tf.identity(op_, name=_TFOUTPUT)
+
+        x_val = np.array([0, 1, 2, 3], dtype=np.int32)
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
+
+        x_val = np.array([0, 1, 2, 3], dtype=np.float32)
+        # can't check the values because in onnx they are padded with 0, in tf they are not
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, check_value=False)
+
+    @check_tf_min_version("2.1")
+    @skip_tflite("TFlite errors on some attributes")
+    @check_opset_min_version(9, "string")
+    def test_string_to_number(self):
+        def func(x):
+            op_ = tf.strings.to_number(x)
+            return tf.identity(op_, name=_TFOUTPUT)
+
+        # tf gets this wrong and returns fp32 instead of int
+        x_val = np.array("123", dtype=np.object)
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
+
+        x_val = np.array("123.1", dtype=np.object)
+        # can't check the values because in onnx they are padded with 0, in tf they are not
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, check_value=False)
 
 
 if __name__ == '__main__':

tf2onnx/onnx_opset/tensor.py

@@ -3799,3 +3799,27 @@ def version_8(cls, ctx, node, **kwargs):
         # broadcast by expanding
         node.type = "Expand"
         ctx.insert_new_node_on_input(node, "Cast", node.input[1], to=TensorProto.INT64)
+
+
+@tf_op("AsString")
+class AsString:
+    @classmethod
+    def version_9(cls, ctx, node, **kwargs):
+        if (node.get_attr_value("precision") or node.get_attr_value("scientific") or node.get_attr_value("fill")):
+            logger.warning(
+                "ONNX does not support precision, scientific and fill attributes for AsString")
+        shapes = node.output_shapes
+        ctx.remove_node(node.name)
+        _ = ctx.make_node("Cast", node.input, name=node.name,
+                          dtypes=[TensorProto.STRING], shapes=shapes, attr={"to": TensorProto.STRING})
+
+
+@tf_op("StringToNumber")
+class StringToNumber:
+    @classmethod
+    def version_9(cls, ctx, node, **kwargs):
+        shapes = node.output_shapes
+        dtypes = node.output_dtypes
+        ctx.remove_node(node.name)
+        _ = ctx.make_node("Cast", node.input, name=node.name,
+                          dtypes=dtypes, shapes=shapes, attr={"to": dtypes[0]})