[Trivial] Fix incorrect pointer usage

The parameters of the functions \'ml_tensors_data_destroy\' and \'ml_tensors_info_destroy\' are both of the types \'ml_tensors_data_h\'.
However, several code are using address values.
Added dereference operator to match parameter type.

Signed-off-by: SeoHyungjun <hyungjun.seo@samsung.com>

diff --git a/api/capi/src/nntrainer.cpp b/api/capi/src/nntrainer.cpp
index b713649e12fe8096b11653e0fcb6166a85929f95..0dc818afa4f6e7281ce81fdfc2e20a9aa3d287fe 100644 (file)
--- a/api/capi/src/nntrainer.cpp
+++ b/api/capi/src/nntrainer.cpp
@@ -189,7 +189,7 @@ static int ml_train_dataset_create(ml_train_dataset_h *dataset,
 template <typename... Args>
 static int ml_train_dataset_add_(ml_train_dataset_h dataset,
                                  ml_train_dataset_mode_e mode,
-                                 ml::train::DatasetType type, Args &&...args) {
+                                 ml::train::DatasetType type, Args &&... args) {
   check_feature_state();
   std::shared_ptr<ml::train::Dataset> underlying_dataset;
 
@@ -1085,20 +1085,20 @@ int ml_train_model_get_input_tensors_info(ml_train_model_h model,
 
   status = ml_tensors_info_set_count(*info, dims.size());
   if (status != ML_ERROR_NONE) {
-    ml_tensors_info_destroy(info);
+    ml_tensors_info_destroy(*info);
     return status;
   }
 
   for (unsigned int i = 0; i < dims.size(); ++i) {
     status = ml_tensors_info_set_tensor_type(*info, i, ML_TENSOR_TYPE_FLOAT32);
     if (status != ML_ERROR_NONE) {
-      ml_tensors_info_destroy(info);
+      ml_tensors_info_destroy(*info);
       return status;
     }
 
     status = ml_tensors_info_set_tensor_dimension(*info, i, dims[i].getDim());
     if (status != ML_ERROR_NONE) {
-      ml_tensors_info_destroy(info);
+      ml_tensors_info_destroy(*info);
       return status;
     }
   }
@@ -1143,20 +1143,20 @@ int ml_train_model_get_output_tensors_info(ml_train_model_h model,
 
   status = ml_tensors_info_set_count(*info, dims.size());
   if (status != ML_ERROR_NONE) {
-    ml_tensors_info_destroy(info);
+    ml_tensors_info_destroy(*info);
     return status;
   }
 
   for (unsigned int i = 0; i < dims.size(); ++i) {
     status = ml_tensors_info_set_tensor_type(*info, i, ML_TENSOR_TYPE_FLOAT32);
     if (status != ML_ERROR_NONE) {
-      ml_tensors_info_destroy(info);
+      ml_tensors_info_destroy(*info);
       return status;
     }
 
     status = ml_tensors_info_set_tensor_dimension(*info, i, dims[i].getDim());
     if (status != ML_ERROR_NONE) {
-      ml_tensors_info_destroy(info);
+      ml_tensors_info_destroy(*info);
       return status;
     }
   }
@@ -1253,33 +1253,33 @@ int ml_train_model_get_weight(ml_train_model_h model, const char *layer_name,
 
   status = ml_tensors_info_set_count(*info, dims.size());
   if (status != ML_ERROR_NONE) {
-    ml_tensors_info_destroy(info);
+    ml_tensors_info_destroy(*info);
     return status;
   }
 
   for (unsigned int i = 0; i < dims.size(); ++i) {
     status = ml_tensors_info_set_tensor_type(*info, i, ML_TENSOR_TYPE_FLOAT32);
     if (status != ML_ERROR_NONE) {
-      ml_tensors_info_destroy(info);
+      ml_tensors_info_destroy(*info);
       return status;
     }
 
     status = ml_tensors_info_set_tensor_dimension(*info, i, dims[i].getDim());
     if (status != ML_ERROR_NONE) {
-      ml_tensors_info_destroy(info);
+      ml_tensors_info_destroy(*info);
       return status;
     }
 
     status = ml_tensors_info_set_tensor_name(*info, i, weight_name[i].c_str());
     if (status != ML_ERROR_NONE) {
-      ml_tensors_info_destroy(info);
+      ml_tensors_info_destroy(*info);
       return status;
     }
   }
 
   status = ml_tensors_data_create(*info, weight);
   if (status != ML_ERROR_NONE) {
-    ml_tensors_data_destroy(weight);
+    ml_tensors_data_destroy(*weight);
     return status;
   }