!5719 [MSLITE]mindspore export inferface finetune lite

Merge pull request !5719 from zhengjun10/master

mindspore/lite/test/models_mindspore.cfg

@@ -1,2 +1,2 @@
-ssd.pb
+#ssd.pb
-mobilenet_v2.pb
+# mobilenet_v2.pb

mindspore/lite/tools/anf_importer/import_from_protobuf.cc

@@ -46,9 +46,6 @@ using uint64 = uint64_t;
 namespace mindspore::lite {
 
 static constexpr char kConstantValueNode[] = "Constant";
-static constexpr char kCNodeShapeAttr[] = "shape";
-static constexpr char kCNodeShape1Attr[] = "shape1";
-static constexpr char kCNodeShape2Attr[] = "shape2";
 
 enum ParseForm : int {
   FORM_PARSE_TYPE = 0,
@@ -57,32 +54,143 @@ enum ParseForm : int {
 };
 
 static std::map<std::string, ParseForm> kParseTypeSwitchMap{
-  {"type", FORM_PARSE_TYPE}, {"scalar", FORM_PARSE_SCALAR}, {"tensor", FORM_PARSE_TENSOR}};
+    {"type", FORM_PARSE_TYPE}, {"scalar", FORM_PARSE_SCALAR}, {"tensor", FORM_PARSE_TENSOR}};
 
 static std::unordered_map<int, TypeId> kDefaultValueSwitchMap{
-  {onnx::TensorProto_DataType_BOOL, kNumberTypeBool},     {onnx::TensorProto_DataType_INT8, kNumberTypeInt8},
+    {onnx::TensorProto_DataType_BOOL, kNumberTypeBool}, {onnx::TensorProto_DataType_INT8, kNumberTypeInt8},
-  {onnx::TensorProto_DataType_INT16, kNumberTypeInt16},   {onnx::TensorProto_DataType_INT32, kNumberTypeInt32},
+    {onnx::TensorProto_DataType_INT16, kNumberTypeInt16}, {onnx::TensorProto_DataType_INT32, kNumberTypeInt32},
-  {onnx::TensorProto_DataType_INT64, kNumberTypeInt64},   {onnx::TensorProto_DataType_UINT8, kNumberTypeUInt8},
+    {onnx::TensorProto_DataType_INT64, kNumberTypeInt64}, {onnx::TensorProto_DataType_UINT8, kNumberTypeUInt8},
-  {onnx::TensorProto_DataType_UINT16, kNumberTypeUInt16}, {onnx::TensorProto_DataType_UINT32, kNumberTypeUInt32},
+    {onnx::TensorProto_DataType_UINT16, kNumberTypeUInt16}, {onnx::TensorProto_DataType_UINT32, kNumberTypeUInt32},
-  {onnx::TensorProto_DataType_UINT64, kNumberTypeUInt64}, {onnx::TensorProto_DataType_FLOAT16, kNumberTypeFloat16},
+    {onnx::TensorProto_DataType_UINT64, kNumberTypeUInt64}, {onnx::TensorProto_DataType_FLOAT16, kNumberTypeFloat16},
-  {onnx::TensorProto_DataType_FLOAT, kNumberTypeFloat32}, {onnx::TensorProto_DataType_DOUBLE, kNumberTypeFloat64},
+    {onnx::TensorProto_DataType_FLOAT, kNumberTypeFloat32}, {onnx::TensorProto_DataType_DOUBLE, kNumberTypeFloat64},
-  {onnx::TensorProto_DataType_STRING, kObjectTypeString},
+    {onnx::TensorProto_DataType_STRING, kObjectTypeString},
 };
 
-#define PARSE_ONNXATTR_IN_SCALAR_FORM(type, valuetype)                                                \
+std::shared_ptr<ValueTuple> ParserScalarAttrValue(const std::string &attr_name,
-  void ParseAttrInScalar_##type##_##valuetype(const PrimitivePtr &prim, const std::string &attr_name, \
+                                                  const std::unordered_map<string, ValuePtr> &kv) {
-                                              const onnx::TensorProto &attr_tensor) {                 \
+  std::string str = attr_name;
-    MS_EXCEPTION_IF_NULL(prim);                                                                       \
+  auto replace = [&](const string &orgStr, const string &newStr) {
-    std::vector<ValuePtr> attr_value_vec;                                                             \
+    std::string::size_type pos(0);
-    for (int i = 0; i < attr_tensor.type##_data_size(); ++i) {                                        \
+    while ((pos = str.find(orgStr)) != std::string::npos) {
-      auto value = static_cast<valuetype>(attr_tensor.type##_data(i));                                \
+      str.replace(pos, orgStr.length(), newStr);
-      attr_value_vec.push_back(MakeValue<valuetype>(value));                                          \
+    }
-    }                                                                                                 \
+    return str;
-    if (attr_value_vec.size() == 1) {                                                                 \
+  };
-      prim->AddAttr(attr_name, attr_value_vec[0]);                                                    \
+  // remove "scalar:"
-    } else {                                                                                          \
+  str = replace("scalar:", "");
-      prim->AddAttr(attr_name, std::make_shared<ValueList>(attr_value_vec));                          \
+  // remove "Tuple"
-    }                                                                                                 \
+  str = replace("Tuple", "");
+  // remove "List"
+  str = replace("List", "");
+  std::stack<std::string> rules;
+  std::stack<ValuePtr> value;
+  int num = 0, count = 0;
+  for (size_t i = 0; i < str.length(); i++) {
+    if (str[i] == '[') {
+      rules.push("[");
+    } else if (str[i] == ']') {
+      // rules
+      std::vector<ValuePtr> vec;
+      while (rules.top() != "[") {
+        rules.pop();
+        vec.push_back(value.top());
+        value.pop();
+      }
+      // pop "["
+      rules.pop();
+      // make tuple for names
+      std::string res = "dummy";
+      // make tuple for values
+      reverse(vec.begin(), vec.end());
+      auto vt = std::make_shared<ValueTuple>(vec);
+      if (rules.empty() && value.empty()) {
+        return vt;
+      }
+      rules.push(res);
+      value.push(vt);
+    } else if (str[i] == ',') {
+      continue;
+    } else {
+      count++;
+      if (str[i + 1] == '[' || str[i + 1] == ']' || str[i + 1] == ',') {
+        auto value_name = str.substr(i - count + 1, count);
+        value.push(kv.at(value_name));
+        rules.push(value_name);
+        count = 0;
+        num++;
+      }
+    }
+  }
+  return {};
+}
+
+std::shared_ptr<abstract::AbstractTuple>
+ParserAttrShape(const std::string &attr_name, const std::unordered_map<string, abstract::AbstractTensorPtr> &kv) {
+  std::string str = attr_name;
+  auto replace = [&](const string &orgStr, const string &newStr) {
+    std::string::size_type pos(0);
+    while ((pos = str.find(orgStr)) != std::string::npos) {
+      str.replace(pos, orgStr.length(), newStr);
+    }
+    return str;
+  };
+  // remove "scalar:"
+  str = replace("shape:", "");
+  // remove "Tuple"
+  str = replace("Tuple", "");
+  // remove "List"
+  str = replace("List", "");
+  std::stack<std::string> rules;
+  std::stack<abstract::AbstractBasePtr> value;
+  int num = 0, count = 0;
+  for (size_t i = 0; i < str.length(); i++) {
+    if (str[i] == '[') {
+      rules.push("[");
+    } else if (str[i] == ']') {
+      // rules
+      std::vector<abstract::AbstractBasePtr> vec;
+      while (rules.top() != "[") {
+        rules.pop();
+        vec.push_back(value.top());
+        value.pop();
+      }
+      // pop "["
+      rules.pop();
+      // make tuple for names
+      std::string res = "dummy";
+      // make tuple for values
+      reverse(vec.begin(), vec.end());
+      auto vt = std::make_shared<abstract::AbstractTuple>(vec);
+      if (rules.empty() && value.empty()) {
+        return vt;
+      }
+      rules.push(res);
+      value.push(vt);
+    } else if (str[i] == ',') {
+      continue;
+    } else {
+      count++;
+      if (str[i + 1] == '[' || str[i + 1] == ']' || str[i + 1] == ',') {
+        auto value_name = str.substr(i - count + 1, count);
+        value.push(kv.at(value_name));
+        rules.push(value_name);
+        count = 0;
+        num++;
+      }
+    }
+  }
+  return {};
+}
+
+#define PARSE_ONNXATTR_IN_SCALAR_FORM(type, valuetype)                                    \
+  ValuePtr ParseAttrInScalar_##type##_##valuetype(const onnx::TensorProto &attr_tensor) { \
+    if (attr_tensor.type##_data_size() == 1) {                                            \
+      auto value = static_cast<valuetype>(attr_tensor.type##_data(0));                    \
+      return MakeValue<valuetype>(value);                                                 \
+    } else {                                                                              \
+      MS_LOG(ERROR) << "size of scalar tensor doesn't equal 1!";                          \
+    }                                                                                     \
+    return {};                                                                            \
   }
 
 PARSE_ONNXATTR_IN_SCALAR_FORM(double, double)
@@ -193,45 +301,34 @@ bool AnfImporterFromProtobuf::ObtainCNodeAttrInTypeForm(const PrimitivePtr &prim
   return true;
 }
 
-bool AnfImporterFromProtobuf::ObtainCNodeAttrInScalarForm(const PrimitivePtr &prim, const std::string &attr_name,
+ValuePtr AnfImporterFromProtobuf::ObtainCNodeAttrInScalarForm(const onnx::TensorProto &attr_tensor) {
-                                                          const onnx::TensorProto &attr_tensor) {
-  MS_EXCEPTION_IF_NULL(prim);
   const int attr_tensor_type = attr_tensor.data_type();
   switch (attr_tensor_type) {
     case onnx::TensorProto_DataType_STRING: {
-      ParseAttrInScalar_string_string(prim, attr_name, attr_tensor);
+      return ParseAttrInScalar_string_string(attr_tensor);
-      break;
     }
     case onnx::TensorProto_DataType_INT32: {
-      ParseAttrInScalar_int32_int32(prim, attr_name, attr_tensor);
+      return ParseAttrInScalar_int32_int32(attr_tensor);
-      break;
     }
     case onnx::TensorProto_DataType_INT64: {
-      ParseAttrInScalar_int64_int64(prim, attr_name, attr_tensor);
+      return ParseAttrInScalar_int64_int64(attr_tensor);
-      break;
     }
     case onnx::TensorProto_DataType_UINT64: {
-      ParseAttrInScalar_uint64_uint64(prim, attr_name, attr_tensor);
+      return ParseAttrInScalar_uint64_uint64(attr_tensor);
-      break;
     }
     case onnx::TensorProto_DataType_FLOAT: {
-      ParseAttrInScalar_float_float(prim, attr_name, attr_tensor);
+      return ParseAttrInScalar_float_float(attr_tensor);
-      break;
     }
     case onnx::TensorProto_DataType_DOUBLE: {
-      ParseAttrInScalar_double_double(prim, attr_name, attr_tensor);
+      return ParseAttrInScalar_double_double(attr_tensor);
-      break;
     }
     case onnx::TensorProto_DataType_BOOL: {
-      ParseAttrInScalar_int32_bool(prim, attr_name, attr_tensor);
+      return ParseAttrInScalar_int32_bool(attr_tensor);
-      auto value = prim->GetAttr(attr_name);
-      break;
     }
-    default:
+    default:MS_LOG(ERROR) << "Obtain attr in scalar-form has not support input type: " << attr_tensor_type;
-      MS_LOG(ERROR) << "Obtain attr in scalar-form has not support input type: " << attr_tensor_type;
+      return {};
-      return false;
   }
-  return true;
+  return {};
 }
 
 bool AnfImporterFromProtobuf::ObtainCNodeAttrInTensorForm(const PrimitivePtr &prim, const std::string &attr_name,
@@ -268,7 +365,6 @@ bool AnfImporterFromProtobuf::ObtainCNodeAttrInTensorForm(const PrimitivePtr &pr
       prim->set_attr(attr_name, MakeValue<bool>(attr_value));
     }
   }
-
   return ret == EOK;
 }
 
@@ -280,22 +376,46 @@ bool AnfImporterFromProtobuf::GetAttrValueForCNode(const PrimitivePtr &prim, con
     return false;
   }
   const std::string &ref_attr_name = attr_proto.ref_attr_name();
-  const onnx::TensorProto &attr_tensor = attr_proto.t();
+  string type;
-  switch (kParseTypeSwitchMap[ref_attr_name]) {
+  std::size_t pos(0);
-    case FORM_PARSE_TYPE: {
+  if ((pos = ref_attr_name.find("scalar:")) != std::string::npos) {
-      return ObtainCNodeAttrInTypeForm(prim, attr_name, attr_tensor);
+    type = ref_attr_name.substr(pos, string("scalar:").length() - 1);
-    }
+  } else if ((pos = ref_attr_name.find("type:")) != std::string::npos) {
-    case FORM_PARSE_SCALAR: {
+    type = ref_attr_name.substr(pos, string("type:").length() - 1);
-      return ObtainCNodeAttrInScalarForm(prim, attr_name, attr_tensor);
+  } else if ((pos = ref_attr_name.find("tensor:")) != std::string::npos) {
-    }
+    type = ref_attr_name.substr(pos, string("tensor:").length() - 1);
-    case FORM_PARSE_TENSOR: {
-      return ObtainCNodeAttrInTensorForm(prim, attr_name, attr_tensor);
-    }
-    default:
-      MS_LOG(ERROR) << "parse attr type don't support input of ref_attr_name";
-      return false;
   }
+  std::unordered_map<std::string, ValuePtr> kv;
+  for (int i = 0; i < attr_proto.tensors_size(); i++) {
+    const onnx::TensorProto &attr_tensor = attr_proto.tensors(i);
+    switch (kParseTypeSwitchMap[type]) {
+      case FORM_PARSE_TYPE: {
+        return ObtainCNodeAttrInTypeForm(prim, attr_name, attr_tensor);
+      }
+      case FORM_PARSE_SCALAR: {
+        auto res = ObtainCNodeAttrInScalarForm(attr_tensor);
+        kv.insert(std::pair<string, ValuePtr>(attr_tensor.name(), res));
+        break;
+      }
+      case FORM_PARSE_TENSOR: {
+        return ObtainCNodeAttrInTensorForm(prim, attr_name, attr_tensor);
+      }
+      default:MS_LOG(ERROR) << "parse attr type don't support input of ref_attr_name";
+        return false;
+    }
+  }
+  if (kParseTypeSwitchMap[type] == FORM_PARSE_SCALAR) {
+    if (kv.size() == 1) {
+      std::unordered_map<std::string, ValuePtr>::iterator iter = kv.begin();
+      prim->AddAttr(attr_name, iter->second);
+    } else {
+      auto res = ParserScalarAttrValue(ref_attr_name, kv);
+      prim->AddAttr(attr_name, res);
+    }
+  }
+  return true;
 }
+
 bool AnfImporterFromProtobuf::ObtainValueNodeInTensorForm(const std::string &value_node_name,
                                                           const onnx::TensorProto &attr_tensor) {
   const int attr_tensor_type = attr_tensor.data_type();
@@ -321,53 +441,6 @@ bool AnfImporterFromProtobuf::ObtainValueNodeInTensorForm(const std::string &val
   return true;
 }
 
-bool AnfImporterFromProtobuf::ObtainValueNodeInScalarForm(const std::string &value_node_name,
-                                                          const onnx::TensorProto &attr_tensor) {
-  const int attr_tensor_type = attr_tensor.data_type();
-  ValuePtr value_ptr = nullptr;
-  switch (attr_tensor_type) {
-    case onnx::TensorProto_DataType_INT32: {
-      std::vector<int32> add_data;
-      for (int i = 0; i < attr_tensor.int32_data_size(); ++i) {
-        add_data.push_back(attr_tensor.int32_data(i));
-      }
-      if (add_data.size() == 1) {
-        value_ptr = MakeValue(add_data[0]);
-      } else if (!add_data.empty()) {
-        value_ptr = MakeValue<std::vector<int32> >(add_data);
-      }
-      break;
-    }
-    case onnx::TensorProto_DataType_FLOAT: {
-      std::vector<float> add_data;
-      for (int i = 0; i < attr_tensor.float_data_size(); ++i) {
-        add_data.push_back(attr_tensor.float_data(i));
-      }
-
-      if (add_data.size() == 1) {
-        value_ptr = MakeValue(add_data[0]);
-      } else if (!add_data.empty()) {
-        value_ptr = MakeValue<std::vector<float> >(add_data);
-      }
-      break;
-    }
-    case onnx::TensorProto_DataType_UNDEFINED: {
-      std::vector<ValuePtr> elems;
-      value_ptr = std::make_shared<ValueTuple>(elems);
-      break;
-    }
-    default:
-      MS_LOG(ERROR) << "Obtain attr in scalar-form has not support input type: " << attr_tensor_type;
-      return false;
-  }
-  auto new_value_node = NewValueNode(value_ptr);
-  MS_EXCEPTION_IF_NULL(new_value_node);
-  new_value_node->set_abstract(value_ptr->ToAbstract());
-  anfnode_build_map_[value_node_name] = new_value_node;
-
-  return true;
-}
-
 bool AnfImporterFromProtobuf::ObtainValueNodeInTypeForm(const std::string &value_node_name,
                                                         const onnx::TensorProto &attr_tensor) {
   const int attr_tensor_type = attr_tensor.data_type();
@@ -382,23 +455,56 @@ bool AnfImporterFromProtobuf::ObtainValueNodeInTypeForm(const std::string &value
   return true;
 }
 
-bool AnfImporterFromProtobuf::GetAttrValueForValueNode(const std::string &ref_attr_name,
+bool AnfImporterFromProtobuf::GetAttrValueForValueNode(const std::string &value_node_name,
-                                                       const std::string &value_node_name,
+                                                       const onnx::AttributeProto &attr_proto) {
-                                                       const onnx::TensorProto &attr_tensor) {
+  if (!attr_proto.has_ref_attr_name()) {
-  switch (kParseTypeSwitchMap[ref_attr_name]) {
+    MS_LOG(ERROR) << "CNode parse attr type has no ref_attr_name";
-    case FORM_PARSE_SCALAR: {
+    return false;
-      return ObtainValueNodeInScalarForm(value_node_name, attr_tensor);
-    }
-    case FORM_PARSE_TENSOR: {
-      return ObtainValueNodeInTensorForm(value_node_name, attr_tensor);
-    }
-    case FORM_PARSE_TYPE: {
-      return ObtainValueNodeInTypeForm(value_node_name, attr_tensor);
-    }
-    default:
-      MS_LOG(ERROR) << "parse ValueNode value don't support input of ref_attr_name";
-      return false;
   }
+  const std::string &ref_attr_name = attr_proto.ref_attr_name();
+  string type;
+  std::size_t pos(0);
+  if ((pos = ref_attr_name.find("scalar:")) != std::string::npos) {
+    type = ref_attr_name.substr(pos, string("scalar:").length() - 1);
+  } else if ((pos = ref_attr_name.find("type:")) != std::string::npos) {
+    type = ref_attr_name.substr(pos, string("type:").length() - 1);
+  } else if ((pos = ref_attr_name.find("tensor:")) != std::string::npos) {
+    type = ref_attr_name.substr(pos, string("tensor:").length() - 1);
+  }
+  std::unordered_map<std::string, ValuePtr> kv;
+  for (int i = 0; i < attr_proto.tensors_size(); i++) {
+    const onnx::TensorProto &attr_tensor = attr_proto.tensors(i);
+    switch (kParseTypeSwitchMap[type]) {
+      case FORM_PARSE_TYPE: {
+        return ObtainValueNodeInTypeForm(value_node_name, attr_tensor);
+      }
+      case FORM_PARSE_SCALAR: {
+        auto res = ObtainCNodeAttrInScalarForm(attr_tensor);
+        kv.insert(std::pair<string, ValuePtr>(attr_tensor.name(), res));
+        break;
+      }
+      case FORM_PARSE_TENSOR: {
+        return ObtainValueNodeInTensorForm(value_node_name, attr_tensor);
+      }
+      default:MS_LOG(ERROR) << "parse attr type don't support input of ref_attr_name";
+        return false;
+    }
+  }
+
+  ValueNodePtr new_value_node;
+  if (kParseTypeSwitchMap[type] == FORM_PARSE_SCALAR) {
+    if (kv.size() == 1) {
+      auto iter = kv.begin();
+      new_value_node = NewValueNode(iter->second);
+      new_value_node->set_abstract(iter->second->ToAbstract());
+    } else {
+      auto value_ptr = ParserScalarAttrValue(ref_attr_name, kv);
+      new_value_node = NewValueNode(value_ptr);
+      new_value_node->set_abstract(value_ptr->ToAbstract());
+    }
+    anfnode_build_map_[value_node_name] = new_value_node;
+  }
+  return true;
 }
 
 bool AnfImporterFromProtobuf::BuildValueNodeForFuncGraph(const onnx::NodeProto &node_proto) {
@@ -408,22 +514,23 @@ bool AnfImporterFromProtobuf::BuildValueNodeForFuncGraph(const onnx::NodeProto &
     MS_LOG(ERROR) << "parse ValueNode  don't have ref_attr_name";
     return false;
   }
-  const std::string &ref_attr_name = attr_proto.ref_attr_name();
+  return GetAttrValueForValueNode(value_node_name, attr_proto);
-  const onnx::TensorProto &attr_tensor = attr_proto.t();
-
-  return GetAttrValueForValueNode(ref_attr_name, value_node_name, attr_tensor);
 }
 
-abstract::AbstractTensorPtr AnfImporterFromProtobuf::GetAbstractForCNode(const onnx::AttributeProto &attr_proto) {
+std::unordered_map<std::string, abstract::AbstractTensorPtr>
-  std::vector<int> shape_vec;
+AnfImporterFromProtobuf::GetAbstractForCNode(const onnx::AttributeProto &attr_proto) {
-  const onnx::TensorProto &attr_tensor = attr_proto.t();
+  std::unordered_map<std::string, abstract::AbstractTensorPtr> kv;
-  for (int i = 0; i < attr_tensor.dims_size(); ++i) {
+  for (int i = 0; i < attr_proto.tensors_size(); i++) {
-    shape_vec.push_back(attr_tensor.dims(i));
+    std::vector<int> shape_vec;
+    const onnx::TensorProto &attr_tensor = attr_proto.tensors(i);
+    for (int j = 0; j < attr_tensor.dims_size(); ++j) {
+      shape_vec.push_back(attr_tensor.dims(j));
+    }
+    auto type_ptr = TypeIdToType(kDefaultValueSwitchMap[attr_tensor.data_type()]);
+    auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vec);
+    kv.insert(std::pair<string, abstract::AbstractTensorPtr>(attr_tensor.name(), abstract_tensor));
   }
-  auto type_ptr = TypeIdToType(kDefaultValueSwitchMap[attr_tensor.data_type()]);
+  return kv;
-  auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vec);
-  MS_EXCEPTION_IF_NULL(abstract_tensor);
-  return abstract_tensor;
 }
 
 CNodePtr AnfImporterFromProtobuf::BuildCNodeForFuncGraph(const FuncGraphPtr &outputFuncGraph,
@@ -437,25 +544,16 @@ CNodePtr AnfImporterFromProtobuf::BuildCNodeForFuncGraph(const FuncGraphPtr &out
   const std::string &node_name = node_proto.output(0);
   const std::string &fullname_with_scope = node_proto.domain();
   const std::string &node_type = node_proto.op_type();
-  PrimitivePtr prim = std::make_shared<Primitive>(node_type);
+  PrimitivePtr prim = std::make_shared<mindspore::Primitive>(node_type);
   MS_EXCEPTION_IF_NULL(prim);
   prim->set_instance_name(node_type);
-
+  std::unordered_map<std::string, abstract::AbstractTensorPtr> kv;
-  abstract::AbstractTensorPtr abstract = nullptr;
+  string shape_ref_attr_name;
-  abstract::AbstractTensorPtr abstract_first = nullptr;
-  abstract::AbstractTensorPtr abstract_second = nullptr;
   for (int i = 0; i < node_proto.attribute_size(); ++i) {
     const onnx::AttributeProto &attr_proto = node_proto.attribute(i);
-    if (attr_proto.name() == kCNodeShapeAttr) {
+    if (attr_proto.ref_attr_name().find("shape:") != string::npos) {
-      abstract = GetAbstractForCNode(attr_proto);
+      shape_ref_attr_name = attr_proto.ref_attr_name();
-      continue;
+      kv = GetAbstractForCNode(attr_proto);
-    }
-    if (attr_proto.name() == kCNodeShape1Attr) {
-      abstract_first = GetAbstractForCNode(attr_proto);
-      continue;
-    }
-    if (attr_proto.name() == kCNodeShape2Attr) {
-      abstract_second = GetAbstractForCNode(attr_proto);
       continue;
     }
     if (!GetAttrValueForCNode(prim, attr_proto)) {
@@ -463,6 +561,7 @@ CNodePtr AnfImporterFromProtobuf::BuildCNodeForFuncGraph(const FuncGraphPtr &out
       return nullptr;
     }
   }
+
   std::vector<AnfNodePtr> inputs;
   inputs.clear();
   for (int i = 0; i < node_proto.input_size(); ++i) {
@@ -481,26 +580,20 @@ CNodePtr AnfImporterFromProtobuf::BuildCNodeForFuncGraph(const FuncGraphPtr &out
   inputs.insert(inputs.begin(), NewValueNode(primitivec_ptr));
   CNodePtr cnode_ptr = outputFuncGraph->NewCNode(inputs);
   MS_EXCEPTION_IF_NULL(cnode_ptr);
-  if (node_type == "LayerNorm") {
+  if (0 == kv.size()) {
-    AbstractBasePtrList elem;
-    elem.push_back(abstract);
-    elem.push_back(abstract_first);
-    elem.push_back(abstract_second);
-    cnode_ptr->set_abstract(std::make_shared<abstract::AbstractTuple>(elem));
-  } else if (node_type == "ArgMaxWithValue") {
-    AbstractBasePtrList elem;
-    elem.push_back(abstract);
-    elem.push_back(abstract_first);
-    cnode_ptr->set_abstract(std::make_shared<abstract::AbstractTuple>(elem));
-  } else if (nullptr == abstract) {
     AbstractBasePtrList elem;
     for (size_t index = 1; index < cnode_ptr->inputs().size(); ++index) {
       elem.push_back(cnode_ptr->input(index)->abstract());
     }
     cnode_ptr->set_abstract(std::make_shared<abstract::AbstractTuple>(elem));
+  } else if (1 == kv.size()) {
+    std::unordered_map<std::string, abstract::AbstractTensorPtr>::iterator iter = kv.begin();
+    cnode_ptr->set_abstract(iter->second);
   } else {
+    auto abstract = ParserAttrShape(shape_ref_attr_name, kv);
     cnode_ptr->set_abstract(abstract);
   }
+
   cnode_ptr->set_fullname_with_scope(fullname_with_scope);
   anfnode_build_map_[node_name] = cnode_ptr;
   return cnode_ptr;
@@ -652,7 +745,7 @@ int AnfImporterFromProtobuf::Import(const schema::QuantType &quantType) {
 
 onnx::ModelProto *AnfImporterFromProtobuf::ReadOnnxFromBinary(const std::string &model_path) {
   auto onnx_model = new onnx::ModelProto;
-  if (ReadProtoFromBinaryFile((const char *)model_path.c_str(), onnx_model) != RET_OK) {
+  if (ReadProtoFromBinaryFile((const char *) model_path.c_str(), onnx_model) != RET_OK) {
     MS_LOG(ERROR) << "Read onnx model file failed, model path: " << model_path;
     return nullptr;
   }

mindspore/lite/tools/anf_importer/import_from_protobuf.h

@@ -42,9 +42,9 @@ class AnfImporterFromProtobuf : public AnfImporter {
   int Import(const schema::QuantType &quantType = schema::QuantType_QUANT_NONE) override;
 
  private:
-  int ConverterConstTensor() override{ return RET_ERROR; };
+  int ConverterConstTensor() override { return RET_ERROR; };
-  int ConverterCNode() override{ return RET_ERROR; };
+  int ConverterCNode() override { return RET_ERROR; };
-  int AddReturnCNode() override{ return RET_ERROR; };
+  int AddReturnCNode() override { return RET_ERROR; };
   bool ParseModelConfigureInfo(const onnx::ModelProto &model_proto);
   bool BuildFuncGraph(const FuncGraphPtr &outputFuncGraph, const onnx::GraphProto &importProto,
                       const schema::QuantType &quantType);
@@ -59,18 +59,15 @@ class AnfImporterFromProtobuf : public AnfImporter {
   bool GetAttrValueForCNode(const PrimitivePtr &prim, const onnx::AttributeProto &attr_proto);
   bool ObtainCNodeAttrInTypeForm(const PrimitivePtr &prim, const std::string &attr_name,
                                  const onnx::TensorProto &attr_tensor);
-  bool ObtainCNodeAttrInScalarForm(const PrimitivePtr &prim, const std::string &attr_name,
+  ValuePtr ObtainCNodeAttrInScalarForm(const onnx::TensorProto &attr_tensor);
-                                   const onnx::TensorProto &attr_tensor);
   bool ObtainCNodeAttrInTensorForm(const PrimitivePtr &prim, const std::string &attr_name,
                                    const onnx::TensorProto &attr_tensor);
   bool BuildValueNodeForFuncGraph(const onnx::NodeProto &node_proto);
   bool ObtainValueNodeInTensorForm(const string &value_node_name, const onnx::TensorProto &attr_tensor);
-
+  bool GetAttrValueForValueNode(const std::string &value_node_name, const onnx::AttributeProto &attr_proto);
-  bool ObtainValueNodeInScalarForm(const string &value_node_name, const onnx::TensorProto &attr_tensor);
-  bool GetAttrValueForValueNode(const string &ref_attr_name, const std::string &value_node_name,
-                                const onnx::TensorProto &attr_tensor);
   bool ObtainValueNodeInTypeForm(const string &value_node_name, const onnx::TensorProto &attr_tensor);
-  abstract::AbstractTensorPtr GetAbstractForCNode(const onnx::AttributeProto &attr_proto);
+  std::unordered_map<std::string,
+                     abstract::AbstractTensorPtr> GetAbstractForCNode(const onnx::AttributeProto &attr_proto);
 
  private:
   std::string producer_name_;