diff --git a/mindspore/lite/src/runtime/kernel/arm/base/quant_dtype_cast.cc b/mindspore/lite/src/runtime/kernel/arm/base/quant_dtype_cast.cc
index 8ef4881b51..cc3a47cbc7 100644
--- a/mindspore/lite/src/runtime/kernel/arm/base/quant_dtype_cast.cc
+++ b/mindspore/lite/src/runtime/kernel/arm/base/quant_dtype_cast.cc
@@ -111,8 +111,10 @@ int QuantDTypeCastCPUKernel::QuantDTypeCast(int task_id) {
     MS_LOG(ERROR) << "QuantDTypeCast need quantization parameters which is not found.";
     return RET_ERROR;
   }
-  auto quant_arg = out_tensors_.front()->quant_params().front().inited ? out_tensors_.front()->quant_params().front()
-                                                                       : in_tensors_.front()->quant_params().front();
+  auto quant_arg =
+    (!out_tensors_.front()->quant_params().empty() && out_tensors_.front()->quant_params().front().inited)
+      ? out_tensors_.front()->quant_params().front()
+      : in_tensors_.front()->quant_params().front();
   int ret = RET_OK;
   if (src_dtype == TypeId::kNumberTypeInt8 && dst_dtype == TypeId::kNumberTypeFloat32) {
     ret = DoDequantizeInt8ToFp32(int8_ptr_ + thread_offset, float32_ptr_ + thread_offset, quant_arg.scale,
diff --git a/mindspore/lite/test/models_mindspore.cfg b/mindspore/lite/test/models_mindspore.cfg
index cca5f36835..d684b289d0 100644
--- a/mindspore/lite/test/models_mindspore.cfg
+++ b/mindspore/lite/test/models_mindspore.cfg
@@ -12,3 +12,6 @@ ocr_mobilenetV2.mindir 1.5
 mobilenet_quant.mindir 5
 mindspore_ghostnet_ssd_13x.mindir 1.5
 mindspore_ghost-nose-pets-811.mindir 0.5
+mindspore_ghost-pets-8244.mindir 1.5
+mindspore_ghostnet600M-pets.mindir 1.5
+mindspore_ghostnet_1x_pets_int8.mindir 12
diff --git a/mindspore/lite/test/models_with_several_inputs_or_without_outputs.cfg b/mindspore/lite/test/models_with_several_inputs_or_without_outputs.cfg
index 1efff5e30c..d749993212 100644
--- a/mindspore/lite/test/models_with_several_inputs_or_without_outputs.cfg
+++ b/mindspore/lite/test/models_with_several_inputs_or_without_outputs.cfg
@@ -27,3 +27,6 @@ mtk_transformer_encoder.tflite
 mtk_transformer_decoder_joint.tflite
 ml_ei_facedetection.onnx
 mobilebert_1_default_1.tflite
+quant_aware_bank_card_detection_inception.onnx
+quant_aware_bank_card_recognition_fcny.onnx
+quant_aware_identify_card_detect.onnx
diff --git a/mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc b/mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc
index 0ee73c9702..25ff498afe 100644
--- a/mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc
+++ b/mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc
@@ -157,7 +157,7 @@ lite::STATUS ReplaceCNode(const FuncGraphPtr &func_graph, const CNodePtr &any_no
       MS_LOG(ERROR) << "CreateNewParamter failed, name: " << input_node->fullname_with_scope();
       return lite::RET_ERROR;
     }
-    new_parameter->set_name(input_node->fullname_with_scope());
+    new_parameter->set_name("constfold_" + input_node->fullname_with_scope());
     manager->Replace(input_node, new_parameter);
   }
   return lite::RET_OK;
diff --git a/mindspore/lite/tools/optimizer/graph/weight_format_hardcode_pass.cc b/mindspore/lite/tools/optimizer/graph/weight_format_hardcode_pass.cc
index 86fe3e6345..5bc468da1b 100644
--- a/mindspore/lite/tools/optimizer/graph/weight_format_hardcode_pass.cc
+++ b/mindspore/lite/tools/optimizer/graph/weight_format_hardcode_pass.cc
@@ -103,6 +103,7 @@ lite::STATUS WeightFormatHardCodePass::HardCodeMS(const AnfNodePtr &conv_node,
                                                   const ParamValueLitePtr &param_value) const {
   MS_ASSERT(conv_cnode != nullptr);
   MS_ASSERT(param_value != nullptr);
+  auto weight_node = conv_node->cast<CNodePtr>()->input(kConvWeightIndex);
   auto op_type = GetCNodeType(conv_node);
   switch (this->quant_type) {
     case QuantType_AwareTraining: {
@@ -121,10 +122,8 @@ lite::STATUS WeightFormatHardCodePass::HardCodeMS(const AnfNodePtr &conv_node,
       if (op_type == schema::PrimitiveType_Conv2D) {
         param_value->set_format(schema::Format::Format_KCHW);
       } else if (op_type == schema::PrimitiveType_DepthwiseConv2D) {
-        // the format is initialized to NUM_OF_FORMAT, and set to NHWC in const folding.
-        if (param_value->format() == schema::Format::Format_NHWC) {
-          param_value->set_format(schema::Format::Format_KCHW);
-        } else {
+        // the format should be set to KCHW while the weight is output of constfolding .
+        if (weight_node->fullname_with_scope().find("constfold") == weight_node->fullname_with_scope().npos) {
           param_value->set_format(schema::Format::Format_CKHW);
         }
       } else if (op_type == schema::PrimitiveType_DeDepthwiseConv2D) {