diff --git a/mindspore/ccsrc/debug/dump_proto.cc b/mindspore/ccsrc/debug/dump_proto.cc
index 4df1fe7cb30..05aae5d13cd 100644
--- a/mindspore/ccsrc/debug/dump_proto.cc
+++ b/mindspore/ccsrc/debug/dump_proto.cc
@@ -235,7 +235,7 @@ void ProtoExporter::SetValueToProto(const ValuePtr &val, irpb::ValueProto *value
     value_proto->set_dtype(irpb::DT_TYPE);
     value_proto->mutable_type_val()->set_data_type(irpb::DT_BASE_COMPLEX);
   } else {
-    MS_LOG(WARNING) << "Unsupported type " << val->type_name();
+    MS_LOG(DEBUG) << "Unsupported type " << val->type_name();
   }
 }
 
@@ -471,7 +471,7 @@ void ProtoExporter::ExportCNode(const FuncGraphPtr &func_graph, const CNodePtr &
 
   // CNode/ConstGraph/Const/Parameter
   if (op->isa<CNode>() || IsValueNode<FuncGraph>(op) || op->isa<Parameter>()) {
-    MS_LOG(WARNING) << "Operator must be a primitive";
+    MS_LOG(DEBUG) << "Operator must be a primitive";
   } else {
     GetOpNodeTypeAndAttrs(func_graph, op, node_proto);
     node_proto->set_name(std::to_string(apply_idx));
diff --git a/tests/ut/python/parameter_feature/test_parameter.py b/tests/ut/python/parameter_feature/test_parameter.py
index 02a64ebd3d4..69327ff97b4 100644
--- a/tests/ut/python/parameter_feature/test_parameter.py
+++ b/tests/ut/python/parameter_feature/test_parameter.py
@@ -17,6 +17,7 @@ import numpy as np
 import mindspore.context as context
 import mindspore.ops.composite as C
 from mindspore import Tensor, Parameter
+from mindspore import nn
 from mindspore.nn import Cell
 from mindspore.ops import operations as P
 
@@ -312,3 +313,23 @@ def test_pass_args_by_key_ward_way():
     grad_net = GradNet(net)
     sens = Tensor(np.ones((3, 3, 4), np.float32))
     grad_net(x, y=y, z=z, sens=sens)
+
+
+def test_none_input():
+    """
+    Feature: Net's inputs
+    Description: Support none input for the outermost net
+    Expectation: no error
+    """
+
+    class Net(Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.op = nn.ResizeBilinear()
+
+        def construct(self, a, b, c, d):
+            return self.op(a, b, c, d)
+
+    x = Tensor(np.array([1, 2, 3, 4]).astype(np.float32).reshape((1, 1, 2, 2,)))
+    net = Net()
+    net(x, (4, 4), None, True)