diff --git a/mindspore/core/ir/dtype/type_id.h b/mindspore/core/ir/dtype/type_id.h
index 028fb5ec89..7933346157 100644
--- a/mindspore/core/ir/dtype/type_id.h
+++ b/mindspore/core/ir/dtype/type_id.h
@@ -78,6 +78,7 @@ enum TypeId : int {
   kNumberTypeFloat16,
   kNumberTypeFloat32,
   kNumberTypeFloat64,
+  kNumberTypeComplex64,
   kNumberTypeEnd
 };
 }  // namespace mindspore
diff --git a/mindspore/lite/schema/model.fbs b/mindspore/lite/schema/model.fbs
index ba529639d0..3705b2ad0a 100644
--- a/mindspore/lite/schema/model.fbs
+++ b/mindspore/lite/schema/model.fbs
@@ -212,6 +212,9 @@ union PrimitiveType {
     CustomExtractFeatures,
     AudioSpectrogram,
     Mfcc,
+    Rfft,
+    FftReal,
+    FftImag,
 }
 
 enum QuantType: int {
diff --git a/mindspore/lite/schema/ops.fbs b/mindspore/lite/schema/ops.fbs
index 874c8f399f..509b4b298b 100644
--- a/mindspore/lite/schema/ops.fbs
+++ b/mindspore/lite/schema/ops.fbs
@@ -987,4 +987,14 @@ table Mfcc {
     freqLowerLimit : float;
     filterBankChannelNum : int;
     dctCoeffNum : int;
-}
\ No newline at end of file
+}
+
+table Rfft {
+    fftLength : int;
+}
+
+table FftReal {
+}
+
+table FftImag {
+}
diff --git a/mindspore/lite/src/lite_kernel.cc b/mindspore/lite/src/lite_kernel.cc
index 43a7a07f69..8ccd593a2d 100644
--- a/mindspore/lite/src/lite_kernel.cc
+++ b/mindspore/lite/src/lite_kernel.cc
@@ -110,7 +110,8 @@ std::vector<lite::Tensor *> LiteKernelUtil::SubgraphInputTensors(const std::vect
   for (const auto &kernel : input_kernels) {
     for (const auto &tensor : kernel->in_tensors()) {
       auto iter = std::find(all_output_tensors.begin(), all_output_tensors.end(), tensor);
-      if (iter == all_output_tensors.end() && tensor->data_c() == nullptr) {
+      if (iter == all_output_tensors.end() &&
+          !(tensor->category() == mindspore::lite::Tensor::CONST && tensor->data_c() != nullptr)) {
         input_tensors.emplace_back(tensor);
       }
     }
diff --git a/mindspore/lite/src/runtime/kernel/arm/fp32/convolution.cc b/mindspore/lite/src/runtime/kernel/arm/fp32/convolution.cc
index ea2cc3d724..92c983d57c 100644
--- a/mindspore/lite/src/runtime/kernel/arm/fp32/convolution.cc
+++ b/mindspore/lite/src/runtime/kernel/arm/fp32/convolution.cc
@@ -171,16 +171,16 @@ kernel::LiteKernel *CpuConvFp32KernelCreator(const std::vector<lite::Tensor *> &
   auto conv_param = reinterpret_cast<ConvParameter *>(op_parameter);
   int kernel_h = conv_param->kernel_h_;
   int kernel_w = conv_param->kernel_w_;
-  conv_param->input_h_ = inputs.front()->Height();
-  conv_param->input_w_ = inputs.front()->Width();
-  conv_param->input_channel_ = inputs.front()->Channel();
-  conv_param->output_h_ = outputs.front()->Height();
-  conv_param->output_w_ = outputs.front()->Width();
-  conv_param->output_channel_ = outputs.front()->Channel();
-  conv_param->op_parameter_.thread_num_ = ctx->thread_num_;
   bool use_winograd = false;
   int out_unit;
   if (primitive != nullptr && primitive->GetInferFlag()) {
+    conv_param->input_h_ = inputs.front()->Height();
+    conv_param->input_w_ = inputs.front()->Width();
+    conv_param->input_channel_ = inputs.front()->Channel();
+    conv_param->output_h_ = outputs.front()->Height();
+    conv_param->output_w_ = outputs.front()->Width();
+    conv_param->output_channel_ = outputs.front()->Channel();
+    conv_param->op_parameter_.thread_num_ = ctx->thread_num_;
     CheckIfUseWinograd(&use_winograd, &out_unit, conv_param);
   }
 
diff --git a/mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.cc b/mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.cc
index 91d1a0fe68..630fdaf9ff 100644
--- a/mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.cc
+++ b/mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.cc
@@ -137,6 +137,49 @@ STATUS TfliteCustomParser::ExtractFeatures(const std::vector<uint8_t> &custom_at
   return RET_OK;
 }
 
+STATUS TfliteCustomParser::Rfft(const std::vector<uint8_t> &custom_attr, schema::CNodeT *op,
+                                const std::unique_ptr<tflite::OperatorT> &tflite_op,
+                                const std::unique_ptr<tflite::ModelT> &tflite_model) {
+  std::unique_ptr<schema::RfftT> attr = std::make_unique<schema::RfftT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  std::vector<int> fft_length;
+  if (GetTfliteData(tflite_op->inputs[1], tflite_model->subgraphs[0]->tensors, tflite_model->buffers, fft_length)) {
+    MS_LOG(ERROR) << "rfft -> fftLength get failed";
+    return RET_ERROR;
+  }
+  attr->fftLength = fft_length[0];
+  op->primitive->value.type = schema::PrimitiveType_Rfft;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+STATUS TfliteCustomParser::FftReal(const std::vector<uint8_t> &custom_attr, schema::CNodeT *op,
+                                   const std::unique_ptr<tflite::OperatorT> &tflite_op) {
+  std::unique_ptr<schema::FftRealT> attr = std::make_unique<schema::FftRealT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  op->primitive->value.type = schema::PrimitiveType_FftReal;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
+STATUS TfliteCustomParser::FftImag(const std::vector<uint8_t> &custom_attr, schema::CNodeT *op,
+                                   const std::unique_ptr<tflite::OperatorT> &tflite_op) {
+  std::unique_ptr<schema::FftImagT> attr = std::make_unique<schema::FftImagT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  op->primitive->value.type = schema::PrimitiveType_FftImag;
+  op->primitive->value.value = attr.release();
+  return RET_OK;
+}
+
 STATUS TfliteCustomParser::Parse(TfliteTensorsInfo *tensors_info, const std::unique_ptr<tflite::OperatorT> &tflite_op,
                                  const std::unique_ptr<tflite::ModelT> &tflite_model, schema::CNodeT *op) {
   MS_LOG(DEBUG) << "parse TfliteCustomParser";
@@ -163,6 +206,12 @@ STATUS TfliteCustomParser::Parse(TfliteTensorsInfo *tensors_info, const std::uni
     status = ExtractFeatures(custom_attr, op, tflite_op);
   } else if (custom_type == "AudioSpectrogram") {
     status = AudioSpectrogram(custom_attr, op, tflite_op);
+  } else if (custom_type == "FlexRFFT") {
+    status = Rfft(custom_attr, op, tflite_op, tflite_model);
+  } else if (custom_type == "FlexReal") {
+    status = FftReal(custom_attr, op, tflite_op);
+  } else if (custom_type == "FlexImag") {
+    status = FftImag(custom_attr, op, tflite_op);
   } else {
     MS_LOG(ERROR) << "the custom op hasn't been supported now";
     status = RET_NOT_FIND_OP;
diff --git a/mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.h b/mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.h
index 91a0c7a669..17ad6a515c 100644
--- a/mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.h
+++ b/mindspore/lite/tools/converter/parser/tflite/tflite_custom_parser.h
@@ -49,6 +49,15 @@ class TfliteCustomParser : public TfliteNodeParser {
 
   STATUS ExtractFeatures(const std::vector<uint8_t> &custom_attr, schema::CNodeT *op,
                          const std::unique_ptr<tflite::OperatorT> &tflite_op);
+
+  STATUS Rfft(const std::vector<uint8_t> &custom_attr, schema::CNodeT *op,
+              const std::unique_ptr<tflite::OperatorT> &tflite_op, const std::unique_ptr<tflite::ModelT> &tflite_model);
+
+  STATUS FftReal(const std::vector<uint8_t> &custom_attr, schema::CNodeT *op,
+                 const std::unique_ptr<tflite::OperatorT> &tflite_op);
+
+  STATUS FftImag(const std::vector<uint8_t> &custom_attr, schema::CNodeT *op,
+                 const std::unique_ptr<tflite::OperatorT> &tflite_op);
 };
 }  // namespace lite
 }  // namespace mindspore
diff --git a/mindspore/lite/tools/converter/parser/tflite/tflite_util.cc b/mindspore/lite/tools/converter/parser/tflite/tflite_util.cc
index 0b103d0d12..635f09e77c 100644
--- a/mindspore/lite/tools/converter/parser/tflite/tflite_util.cc
+++ b/mindspore/lite/tools/converter/parser/tflite/tflite_util.cc
@@ -133,12 +133,12 @@ std::map<tflite::ActivationFunctionType, schema::ActivationType> tfMsActivationF
 };
 
 std::map<int, TypeId> type_map = {
-  {tflite::TensorType_FLOAT64, TypeId::kNumberTypeFloat64}, {tflite::TensorType_FLOAT32, TypeId::kNumberTypeFloat32},
-  {tflite::TensorType_FLOAT16, TypeId::kNumberTypeFloat16}, {tflite::TensorType_INT32, TypeId::kNumberTypeInt32},
-  {tflite::TensorType_INT16, TypeId::kNumberTypeInt16},     {tflite::TensorType_INT8, TypeId::kNumberTypeInt8},
-  {tflite::TensorType_INT64, TypeId::kNumberTypeInt64},     {tflite::TensorType_UINT8, TypeId::kNumberTypeUInt8},
-  {tflite::TensorType_BOOL, TypeId::kNumberTypeBool},       {tflite::TensorType_STRING, TypeId::kObjectTypeString},
-};
+  {tflite::TensorType_FLOAT64, TypeId::kNumberTypeFloat64},    {tflite::TensorType_FLOAT32, TypeId::kNumberTypeFloat32},
+  {tflite::TensorType_FLOAT16, TypeId::kNumberTypeFloat16},    {tflite::TensorType_INT32, TypeId::kNumberTypeInt32},
+  {tflite::TensorType_INT16, TypeId::kNumberTypeInt16},        {tflite::TensorType_INT8, TypeId::kNumberTypeInt8},
+  {tflite::TensorType_INT64, TypeId::kNumberTypeInt64},        {tflite::TensorType_UINT8, TypeId::kNumberTypeUInt8},
+  {tflite::TensorType_BOOL, TypeId::kNumberTypeBool},          {tflite::TensorType_STRING, TypeId::kObjectTypeString},
+  {tflite::TensorType_COMPLEX64, TypeId::kNumberTypeComplex64}};
 
 schema::ActivationType GetActivationFunctionType(tflite::ActivationFunctionType tfliteAFType) {
   return tfMsActivationFunctionMap.at(tfliteAFType);