fix bug for converter_flags

mindspore/lite/tools/converter/converter_flags.cc

@@ -29,12 +29,12 @@ Flags::Flags() {
   AddFlag(&Flags::outputFile, "outputFile", "Output model file path. Will add .ms automatically", "");
   AddFlag(&Flags::weightFile, "weightFile",
           "Input model weight file path. Needed when fmk is CAFFE. CAFFE: *.caffemodel", "");
-  AddFlag(&Flags::inferenceType, "inferenceType",
-          "Real data type saved in output file, reserved param, NOT used for now. FLOAT | FP16 | UINT8", "FLOAT");
-  AddFlag(&Flags::quantTypeIn, "quantType", "Quantization Type. AwareTraining | WeightQuant | PostTraining", "");
-  AddFlag(&Flags::inputInferenceTypeIn, "inputInferenceType", "Input inference data type. FLOAT | UINT8", "FLOAT");
+  AddFlag(&Flags::inferenceTypeIn, "inferenceType",
+          "Real data type saved in output file, reserved param, NOT used for now. FLOAT | INT8", "FLOAT");
+  AddFlag(&Flags::quantTypeIn, "quantType", "Quantization Type. AwareTraining | PostTraining", "");
+  AddFlag(&Flags::inputInferenceTypeIn, "inputInferenceType", "Input inference data type. FLOAT | INT8", "FLOAT");
   AddFlag(&Flags::stdDev, "stdDev", "Standard deviation value for aware-quantization", "128");
-  AddFlag(&Flags::mean, "mean", "Mean value for aware-quantization", "127");
+  AddFlag(&Flags::mean, "mean", "Mean value for aware-quantization", "-0.5");
   AddFlag(&Flags::quantSize, "quantSize", "Weight quantization size threshold", "0");
   AddFlag(&Flags::configFile, "config_file", "Configuration for post-training.", "");
   AddFlag(&Flags::formatTrans, "formatTrans", "whether transform format. true | false", "true");
@@ -77,14 +77,24 @@ int Flags::Init(int argc, const char **argv) {
   }
   if (this->inputInferenceTypeIn == "FLOAT") {
     this->inputInferenceType = TypeId::kNumberTypeFloat;
-  } else if (this->inputInferenceTypeIn == "UINT8") {
-    this->inputInferenceType = TypeId::kNumberTypeUInt8;
   } else if (this->inputInferenceTypeIn == "INT8") {
     this->inputInferenceType = TypeId::kNumberTypeInt8;
   } else {
-    std::cerr << "INPUT INVALID: inputInferenceType is invalid: %s", this->inputInferenceTypeIn.c_str();
+    std::cerr << "INPUT INVALID: inputInferenceType is invalid: %s, supported inputInferenceType: FLOAT | INT8",
+      this->inputInferenceTypeIn.c_str();
     return 1;
   }
+
+  if (this->inferenceTypeIn == "FLOAT") {
+    this->inferenceType = TypeId::kNumberTypeFloat;
+  } else if (this->inferenceTypeIn == "INT8") {
+    this->inferenceType = TypeId::kNumberTypeInt8;
+  } else {
+    std::cerr << "INPUT INVALID: inferenceType is invalid: %s, supported inferenceType: FLOAT | INT8",
+      this->inferenceTypeIn.c_str();
+    return 1;
+  }
+
   if (this->fmkIn == "CAFFE") {
     this->fmk = FmkType_CAFFE;
   } else if (this->fmkIn == "MS") {

mindspore/lite/tools/converter/converter_flags.h

@@ -63,10 +63,10 @@ class Flags : public virtual mindspore::lite::FlagParser {
   // used for quantization
   std::string quantTypeIn;
   QuantType quantType;
-  std::string inferenceType;
+  std::string inferenceTypeIn;
+  TypeId inferenceType = TypeId::kNumberTypeFloat;
   // used for parse aware trainning
   std::string inputInferenceTypeIn;
-  //  mindspore::predict::DataType inputInferenceType = DataType_DT_FLOAT;
   TypeId inputInferenceType = TypeId::kNumberTypeFloat;
   std::string stdDev;
   std::string mean;

mindspore/lite/tools/converter/graphdef_transform.cc

@@ -194,6 +194,7 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
       return RET_ERROR;
     }
     dTypeTransPass->SetInputDataDType(ctx.inputInferenceType);
+    dTypeTransPass->SetOutputDataDType(ctx.inferenceType);
     quantNodeOptimizer.AddPass(dTypeTransPass);
     quantNodeOptimizer.AddPass(new (std::nothrow) QuantCastFusionPass());
     quantNodeOptimizer.AddPass(new (std::nothrow) IsolatedNodeRemovePass());

mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc

@@ -101,7 +101,7 @@ STATUS DTypeTransPass::DoModelInputDTypeTrans(schema::MetaGraphT *graph) {
 
 STATUS DTypeTransPass::DoModelOutputDTypeTrans(schema::MetaGraphT *graph) {
   MS_ASSERT(graph != nullptr);
-  if (inputDataDType == TypeId::kNumberTypeInt8) {
+  if (outputDataDType == TypeId::kNumberTypeInt8) {
     return RET_OK;
   }
   MS_ASSERT(inputDataDType == TypeId::kNumberTypeFloat);
@@ -231,5 +231,8 @@ NodeIter DTypeTransPass::InsertDTypeTransNode(schema::MetaGraphT *graph, NodeIte
 }
 
 void DTypeTransPass::SetInputDataDType(TypeId dataType) { this->inputDataDType = dataType; }
+
+void DTypeTransPass::SetOutputDataDType(TypeId dataType) { this->outputDataDType = dataType; }
+
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.h

@@ -38,6 +38,8 @@ class DTypeTransPass : public GraphPass {
 
   void SetInputDataDType(TypeId dataType);
 
+  void SetOutputDataDType(TypeId dataType);
+
  private:
   STATUS DoModelInputDTypeTrans(schema::MetaGraphT *graph);
 
@@ -51,6 +53,7 @@ class DTypeTransPass : public GraphPass {
  private:
   size_t id;
   TypeId inputDataDType = TypeId::kNumberTypeFloat;
+  TypeId outputDataDType = TypeId::kNumberTypeFloat;
 
   OpDefCopyer castOpCopyer = [](schema::CNodeT *inCNode) -> std::unique_ptr<schema::CNodeT> {
     std::unique_ptr<schema::CNodeT> newCNode(new (std::nothrow) schema::CNodeT);

mindspore/lite/tools/converter/quantizer/aware_quantizer.cc

@@ -88,7 +88,7 @@ AwareQuantizer::AwareQuantizer(schema::MetaGraphT *graph,
   if (inputInferType == "FLOAT") {
     inArr.reset(new (std::nothrow) InputArray(mean, stdValue));
   } else {
-    inArr.reset(new (std::nothrow) InputArray(mean, stdValue, TypeId::kNumberTypeUInt8));
+    inArr.reset(new (std::nothrow) InputArray(mean, stdValue, TypeId::kNumberTypeInt8));
   }
   mInputArray = inArr.get();
   mInputArray->InitQuantParam();

mindspore/lite/tools/converter/quantizer/aware_quantizer.h

@@ -37,8 +37,8 @@ struct InputArray {
   InputArray(float mean, float stdDev,
              TypeId dataType = TypeId::kNumberTypeFloat) {
     this->dataType = dataType;
-    constexpr float qmin = 0;
-    constexpr float qmax = 255;
+    constexpr float qmin = -128;
+    constexpr float qmax = 127;
     mMin = (qmin - mean) / stdDev;
     mMax = (qmax - mean) / stdDev;
   }

mindspore/lite/tools/converter/quantizer/quantize_util.cc

@@ -246,8 +246,8 @@ STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, doubl
     return RET_OK;
   }
 
-  int quantMin = narrowRange ? 1 : 0;
-  int quantMax = (1 << (unsigned int) numBits) - 1;
+  int quantMin = narrowRange ? 1 : 0 - 128;
+  int quantMax = (1 << (unsigned int) numBits) - 1 - 128;
   auto quantMinFloat = static_cast<double>(quantMin);
   auto quantMaxFloat = static_cast<double>(quantMax);
   double scale = (mMax - mMin) / (quantMaxFloat - quantMinFloat);