diff --git a/mindspore/lite/test/config/models_onnx.cfg b/mindspore/lite/test/config/models_onnx.cfg
index 50df3b5213e..96197e39920 100644
--- a/mindspore/lite/test/config/models_onnx.cfg
+++ b/mindspore/lite/test/config/models_onnx.cfg
@@ -84,3 +84,4 @@ ml_video_edit_art_generate_20210513.onnx
 ml_asr_encoder_int8_202103.onnx
 rpnt_pdr_conv2d_16_fixed_last.onnx
 hdc_efficientnet_b3_1w_class.onnx
+yolov5s.onnx
diff --git a/mindspore/lite/test/config/models_onnx_fp16.cfg b/mindspore/lite/test/config/models_onnx_fp16.cfg
index 8b6dce2a72d..9f549dbb00c 100644
--- a/mindspore/lite/test/config/models_onnx_fp16.cfg
+++ b/mindspore/lite/test/config/models_onnx_fp16.cfg
@@ -93,3 +93,4 @@ ml_asr_encoder_int8_202103.onnx 2.1
 # The input range of hdc_efficientnet_b3_1w_class.onnx is [-5, 5], the computation of middle layers contains small
 # values(<1e-5), The fp16 computation precision is low in this case.
 hdc_efficientnet_b3_1w_class.onnx 18
+yolov5s.onnx 2
diff --git a/mindspore/lite/tools/optimizer/graph/unify_format_pass.cc b/mindspore/lite/tools/optimizer/graph/unify_format_pass.cc
index fa7b861a77f..7b323b83ee2 100644
--- a/mindspore/lite/tools/optimizer/graph/unify_format_pass.cc
+++ b/mindspore/lite/tools/optimizer/graph/unify_format_pass.cc
@@ -150,6 +150,10 @@ void ConvertNcTensor2Nh(const FuncGraphPtr &func_graph, const CNodePtr &cnode, s
   lite::DataInfo data_info;
   int status;
   if (utils::isa<ParameterPtr>(cnode->input(index))) {
+    auto input_node = cnode->input(index)->cast<ParameterPtr>();
+    if (!input_node->has_default()) {
+      return;
+    }
     status = lite::FetchDataFromParameterNode(cnode, index, fmk_type, train_flag, &data_info);
   } else {
     status = lite::FetchDataFromValueNode(cnode, index, fmk_type, train_flag, &data_info);
@@ -161,7 +165,7 @@ void ConvertNcTensor2Nh(const FuncGraphPtr &func_graph, const CNodePtr &cnode, s
       (data_info.data_type_ != kNumberTypeFloat32 && data_info.data_type_ != kNumberTypeFloat)) {
     return;
   }
-  std::vector<int> new_shape;
+  std::vector<int> new_shape = data_info.shape_;
   if (data_info.shape_.size() == 1) {
     new_shape = {1, 1, 1, data_info.shape_[0]};
   } else if (data_info.shape_.size() == 2) {