diff --git a/mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc b/mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc
index 7ff75ba0b4c..7bbe8af23ac 100644
--- a/mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc
+++ b/mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc
@@ -63,7 +63,7 @@ kernel::LiteKernel *CpuFullConnectionFp32KernelCreator(const std::vector<lite::T
   auto *weight_tensor = inputs.at(kWeightIndex);
   // data of second tensor of fc may be nullptr
   auto *restore_data = weight_tensor->data_c();
-  if (!weight_tensor->GetQuantParams().empty()) {
+  if (!weight_tensor->GetQuantParams().empty() && restore_data != nullptr) {
     auto *dequant_weight = kernel::LiteKernelUtil::DequantWeight(weight_tensor);
     if (dequant_weight == nullptr) {
       MS_LOG(ERROR) << "dequant data is nullptr.";
@@ -91,7 +91,7 @@ kernel::LiteKernel *CpuFullConnectionFp32KernelCreator(const std::vector<lite::T
     }
     return nullptr;
   }
-  if (!weight_tensor->GetQuantParams().empty()) {
+  if (!weight_tensor->GetQuantParams().empty() && restore_data != nullptr) {
     weight_tensor->FreeData();
     weight_tensor->SetData(restore_data);
   }
diff --git a/mindspore/lite/tools/common/node_util.cc b/mindspore/lite/tools/common/node_util.cc
index b1d8d970ce6..4d3a4e45c90 100644
--- a/mindspore/lite/tools/common/node_util.cc
+++ b/mindspore/lite/tools/common/node_util.cc
@@ -93,7 +93,7 @@ std::vector<schema::PrimitiveType> GetNhwcAllInputOpList() { return nhwcOpAllInp
 
 std::vector<schema::PrimitiveType> GetUint8NhwcOpList() { return int8NeedNhwcOpList; }
 
-std::vector<schema::PrimitiveType> GetUint8OpList() { return int8OpList; }
+std::vector<schema::PrimitiveType> GetInt8OpList() { return int8OpList; }
 
 STATUS NodeUtils::ConvertDims(mindspore::schema::Format src_format, const std::vector<int32_t> &src_dims,
                               mindspore::schema::Format dst_format, std::vector<int32_t> *dst_dims) {
diff --git a/mindspore/lite/tools/common/node_util.h b/mindspore/lite/tools/common/node_util.h
index 2e096a541ac..0a7bc94a9f0 100644
--- a/mindspore/lite/tools/common/node_util.h
+++ b/mindspore/lite/tools/common/node_util.h
@@ -42,7 +42,7 @@ std::vector<schema::PrimitiveType> Getfp32FullOpList();
 
 std::vector<schema::PrimitiveType> GetUint8NhwcOpList();
 
-std::vector<schema::PrimitiveType> GetUint8OpList();
+std::vector<schema::PrimitiveType> GetInt8OpList();
 
 class NodeUtils {
  public:
diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc
index 9c2b4b924a9..05cfb8e9549 100644
--- a/mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc
+++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc
@@ -51,13 +51,7 @@ STATUS DTypeTransPass::Run(schema::MetaGraphT *graph) {
 
 STATUS DTypeTransPass::DoModelInputDTypeTrans(schema::MetaGraphT *graph) {
   MS_ASSERT(graph != nullptr);
-  // modify inputTensor first
   auto &graphInIdxes = graph->inputIndex;
-  for (auto graphInIdx : graphInIdxes) {
-    MS_ASSERT(graph->allTensors.size() > graphInIdx);
-    auto &graphInTensor = graph->allTensors.at(graphInIdx);
-    graphInTensor->dataType = TypeId::kNumberTypeInt8;
-  }
 
   if (this->inputDataDType == TypeId::kNumberTypeInt8) {
     return RET_OK;
@@ -70,7 +64,7 @@ STATUS DTypeTransPass::DoModelInputDTypeTrans(schema::MetaGraphT *graph) {
   for (auto graphInIdx : graphInIdxes) {
     MS_ASSERT(graphInIdx < graph->allTensors.size());
     auto &tensor = graph->allTensors.at(graphInIdx);
-    if (tensor->dims.size() != kNHWCDimNumber) {
+    if (tensor->dims.size() != kNHWCDimNumber || tensor->dataType != kNumberTypeInt8) {
       continue;
     }
 
@@ -137,7 +131,7 @@ STATUS DTypeTransPass::DoNodeInoutDTypeTrans(schema::MetaGraphT *graph) {
   MS_ASSERT(graph != nullptr);
   // insert transNode before and after existNode
   for (auto iter = graph->nodes.begin(); iter != graph->nodes.end(); iter++) {
-    if (IsContain(GetUint8OpList(), GetCNodeTType(**iter)) && (*iter)->quantType == QuantType_AwareTraining) {
+    if (IsContain(GetInt8OpList(), GetCNodeTType(**iter)) && (*iter)->quantType == QuantType_AwareTraining) {
       continue;
     }
     if (GetCNodeTType(**iter) == PrimitiveType_QuantDTypeCast) {
@@ -157,10 +151,16 @@ STATUS DTypeTransPass::DoNodeInoutDTypeTrans(schema::MetaGraphT *graph) {
     for (size_t i = 0; i < (*iter)->inputIndex.size(); i++) {
       MS_ASSERT(graph->allTensors.size() > (*iter)->inputIndex.at(i));
       auto &preTensor = graph->allTensors.at((*iter)->inputIndex.at(i));
+      if (preTensor->dataType == TypeId::kNumberTypeInt || preTensor->dataType == TypeId::kNumberTypeInt32) {
+        continue;
+      }
       auto &graphInIdxes = graph->inputIndex;
       if (!preTensor->data.empty() && !IsContain(graphInIdxes, (*iter)->inputIndex.at(i))) {
         continue;
       }
+      if (IsContain(graphInIdxes, (*iter)->inputIndex.at(i))) {
+        continue;
+      }
       iter = InsertDTypeTransNode(graph, iter, kBefore, i, kInt8ToFP32, &status);
       if (status != RET_OK) {
         MS_LOG(ERROR) << "InsertInt8ToFloat32Node before " << nodeName.c_str() << " failed";
@@ -170,6 +170,10 @@ STATUS DTypeTransPass::DoNodeInoutDTypeTrans(schema::MetaGraphT *graph) {
 
     if (needInsertPost) {
       for (size_t i = 0; i < (*iter)->outputIndex.size(); i++) {
+        auto &postTensor = graph->allTensors.at((*iter)->outputIndex.at(i));
+        if (postTensor->dataType == TypeId::kNumberTypeInt || postTensor->dataType == TypeId::kNumberTypeInt32) {
+          continue;
+        }
         iter = InsertDTypeTransNode(graph, iter, kAfter, i, kFP32ToInt8, &status);
         if (status != RET_OK) {
           MS_LOG(ERROR) << "InsertFloat32ToUint8Node after " << nodeName.c_str() << " failed";
diff --git a/mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.cc b/mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.cc
index 564fa4f1388..82e4eb5d06f 100644
--- a/mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.cc
+++ b/mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.cc
@@ -79,6 +79,7 @@ void TfliteModelParser::SetTensorQuantParam(const std::unique_ptr<tflite::Tensor
   // change quant param min to 0 to fit ms-lite ops
   if (GetTfliteDataType(tflite_tensor->type) == TypeId::kNumberTypeUInt8 && tensor->data.empty()) {
     quant_param->zeroPoint = quant_param->zeroPoint - 128;
+    tensor->dataType = TypeId::kNumberTypeInt8;
   }
 
   if (!tflite_tensor->quantization->min.empty()) {
@@ -164,11 +165,7 @@ STATUS TfliteModelParser::ConvertTensor(const std::unique_ptr<tflite::SubGraphT>
           MS_LOG(ERROR) << "obtain const tensor failed";
           return status;
       }
-    } else if (quantType == QuantType_AwareTraining && tensor->dataType == TypeId::kNumberTypeUInt8) {
-      // set in/out tensor to int8 to fit ms-lite op
-      tensor->dataType = TypeId::kNumberTypeInt8;
     }
-
     // set tensor attr
     if (isInput || isConst) {
       tensor->nodeType = schema::NodeType::NodeType_ValueNode;
diff --git a/mindspore/lite/tools/converter/quantizer/aware_quantizer.cc b/mindspore/lite/tools/converter/quantizer/aware_quantizer.cc
index 21f16e4ba8f..0e40fba1319 100644
--- a/mindspore/lite/tools/converter/quantizer/aware_quantizer.cc
+++ b/mindspore/lite/tools/converter/quantizer/aware_quantizer.cc
@@ -145,7 +145,7 @@ STATUS AwareQuantizer::GenerateQuantParam() {
 STATUS AwareQuantizer::DoQuantize() {
   for (auto iter = graph->nodes.begin(); iter != graph->nodes.end(); iter++) {
     auto &node = *iter;
-    if (!IsContain(GetUint8OpList(), GetCNodeTType(*node))) {
+    if (!IsContain(GetInt8OpList(), GetCNodeTType(*node))) {
       continue;
     }
     if (node->quantType != schema::QuantType_AwareTraining) {
@@ -388,7 +388,7 @@ STATUS AwareQuantizer::DetermineNodeQuantType() {
       }
     }
 
-    if (canQuant && IsContain(GetUint8OpList(), GetCNodeTType(*node))) {
+    if (canQuant && IsContain(GetInt8OpList(), GetCNodeTType(*node))) {
       node->quantType = schema::QuantType_AwareTraining;
     } else {
       node->quantType = schema::QuantType_QUANT_NONE;