[Trivial] dataset usage enum rename

This patch renames enumeration as discussed ahead of time

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

diff --git a/Applications/Custom/LayerClient/jni/main.cpp b/Applications/Custom/LayerClient/jni/main.cpp
index bb9b294..1d65d31 100644 (file)
--- a/Applications/Custom/LayerClient/jni/main.cpp
+++ b/Applications/Custom/LayerClient/jni/main.cpp
@@ -85,8 +85,7 @@ static int ini_model_run(const std::string &ini_path) {
     return 1;
   }
 
-  if (model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN, dataset) !=
-      0) {
+  if (model->setDataset(ml::train::DatasetModeType::MODE_TRAIN, dataset) != 0) {
     std::cerr << "failed to set datatset";
     return 1;
   }
@@ -131,8 +130,7 @@ int api_model_run() {
     return 1;
   }
 
-  if (model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN, dataset) !=
-      0) {
+  if (model->setDataset(ml::train::DatasetModeType::MODE_TRAIN, dataset) != 0) {
     std::cerr << "failed to set datatset";
     return 1;
   }

diff --git a/Applications/LogisticRegression/jni/main.cpp b/Applications/LogisticRegression/jni/main.cpp
index 618dc64..6a83d17 100644 (file)
--- a/Applications/LogisticRegression/jni/main.cpp
+++ b/Applications/LogisticRegression/jni/main.cpp
@@ -192,7 +192,7 @@ int main(int argc, char *argv[]) {
   }
 
   if (training) {
-    NN.setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN,
+    NN.setDataset(ml::train::DatasetModeType::MODE_TRAIN,
                   std::move(data_train));
 
     try {

diff --git a/Applications/MNIST/jni/main.cpp b/Applications/MNIST/jni/main.cpp
index e9d5ee0..c5a3b06 100644 (file)
--- a/Applications/MNIST/jni/main.cpp
+++ b/Applications/MNIST/jni/main.cpp
@@ -308,9 +308,8 @@ int main(int argc, char *argv[]) {
     model->compile();
     model->initialize();
     model->readModel();
-    model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN,
-                      dataset_train);
-    model->setDataset(ml::train::DatasetDataUsageType::DATA_VAL, dataset_val);
+    model->setDataset(ml::train::DatasetModeType::MODE_TRAIN, dataset_train);
+    model->setDataset(ml::train::DatasetModeType::MODE_VALID, dataset_val);
   } catch (std::exception &e) {
     std::cerr << "Error during init " << e.what() << std::endl;
     return 1;

diff --git a/Applications/ProductRatings/jni/main.cpp b/Applications/ProductRatings/jni/main.cpp
index b1ef78e..539c2ff 100644 (file)
--- a/Applications/ProductRatings/jni/main.cpp
+++ b/Applications/ProductRatings/jni/main.cpp
@@ -219,8 +219,8 @@ int main(int argc, char *argv[]) {
   }
 
   if (training) {
-    NN.setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN, dataset_train);
-    NN.setDataset(ml::train::DatasetDataUsageType::DATA_VAL, dataset_val);
+    NN.setDataset(ml::train::DatasetModeType::MODE_TRAIN, dataset_train);
+    NN.setDataset(ml::train::DatasetModeType::MODE_VALID, dataset_val);
     try {
       NN.train({"batch_size=" + std::to_string(batch_size)});
     } catch (std::exception &e) {

diff --git a/Applications/Resnet/jni/main.cpp b/Applications/Resnet/jni/main.cpp
index 390276e..010aa03 100644 (file)
--- a/Applications/Resnet/jni/main.cpp
+++ b/Applications/Resnet/jni/main.cpp
@@ -234,9 +234,9 @@ void createAndRun(unsigned int epochs, unsigned int batch_size,
   auto dataset_valid = ml::train::createDataset(
     ml::train::DatasetType::GENERATOR, validData_cb, valid_user_data.get());
 
-  model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN,
+  model->setDataset(ml::train::DatasetModeType::MODE_TRAIN,
                     std::move(dataset_train));
-  model->setDataset(ml::train::DatasetDataUsageType::DATA_VAL,
+  model->setDataset(ml::train::DatasetModeType::MODE_VALID,
                     std::move(dataset_valid));
 
   model->train();

diff --git a/Applications/SimpleShot/task_runner.cpp b/Applications/SimpleShot/task_runner.cpp
index bbc7099..4eafcdd 100644 (file)
--- a/Applications/SimpleShot/task_runner.cpp
+++ b/Applications/SimpleShot/task_runner.cpp
@@ -253,13 +253,13 @@ int main(int argc, char **argv) {
     return 1;
   }
 
-  if (model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN,
+  if (model->setDataset(ml::train::DatasetModeType::MODE_TRAIN,
                         train_dataset)) {
     std::cerr << "failed to set train dataset" << std::endl;
     return 1;
   };
 
-  if (model->setDataset(ml::train::DatasetDataUsageType::DATA_VAL,
+  if (model->setDataset(ml::train::DatasetModeType::MODE_VALID,
                         valid_dataset)) {
     std::cerr << "failed to set valid dataset" << std::endl;
     return 1;

diff --git a/Applications/TransferLearning/CIFAR_Classification/jni/main_func.cpp b/Applications/TransferLearning/CIFAR_Classification/jni/main_func.cpp
index 8030694..3bb3d8e 100644 (file)
--- a/Applications/TransferLearning/CIFAR_Classification/jni/main_func.cpp
+++ b/Applications/TransferLearning/CIFAR_Classification/jni/main_func.cpp
@@ -308,8 +308,8 @@ int main(int argc, char *argv[]) {
     std::cerr << "Error during readModel, reason: " << e.what() << std::endl;
     return 1;
   }
-  model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN, dataset_train);
-  model->setDataset(ml::train::DatasetDataUsageType::DATA_VAL, dataset_val);
+  model->setDataset(ml::train::DatasetModeType::MODE_TRAIN, dataset_train);
+  model->setDataset(ml::train::DatasetModeType::MODE_VALID, dataset_val);
 
   /**
    * @brief     Neural Network Train & validation

diff --git a/Applications/VGG/jni/main.cpp b/Applications/VGG/jni/main.cpp
index 0fccefb..26224d6 100644 (file)
--- a/Applications/VGG/jni/main.cpp
+++ b/Applications/VGG/jni/main.cpp
@@ -424,10 +424,8 @@ int main(int argc, char *argv[]) {
 
   try {
     NN.readModel();
-    NN.setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN,
-                  std::move(db_train));
-    NN.setDataset(ml::train::DatasetDataUsageType::DATA_VAL,
-                  std::move(db_valid));
+    NN.setDataset(ml::train::DatasetModeType::MODE_TRAIN, std::move(db_train));
+    NN.setDataset(ml::train::DatasetModeType::MODE_VALID, std::move(db_valid));
     NN.train();
     training_loss = NN.getTrainingLoss();
     validation_loss = NN.getValidationLoss();

diff --git a/api/capi/include/nntrainer.h b/api/capi/include/nntrainer.h
index 1967606..2e70d00 100644 (file)
--- a/api/capi/include/nntrainer.h
+++ b/api/capi/include/nntrainer.h
@@ -441,6 +441,7 @@ int ml_train_dataset_create(ml_train_dataset_h *dataset);
  * single element per call to the dataset.
  * @since_tizen 6.5
  * @param[in] dataset The NNTrainer dataset handle.
+ * @param[in] mode The phase where this generator should be used.
  * @param[in] usage The phase where this generator should be used.
  * @param[in] cb Callback to be used for the generator.
  * @param[in] user_data user_data to be fed when @a cb is being called.
@@ -450,7 +451,7 @@ int ml_train_dataset_create(ml_train_dataset_h *dataset);
  * @retval #ML_ERROR_INVALID_PARAMETER Invalid parameter.
  */
 int ml_train_dataset_add_generator(ml_train_dataset_h dataset,
-                                   ml_train_dataset_data_usage_e usage,
+                                   ml_train_dataset_mode_e mode,
                                    ml_train_datagen_cb cb, void *user_data);
 
 /**
@@ -463,7 +464,7 @@ int ml_train_dataset_add_generator(ml_train_dataset_h dataset,
  * privilege %http://tizen.org/privilege/externalstorage. If you can access both
  * storage, you must add all privilege
  * @param[in] dataset The NNTrainer dataset handle.
- * @param[in] usage The phase where this file should be used.
+ * @param[in] mode The phase where this file should be used.
  * @param[in] file file path.
  * @return @c 0 on success. Otherwise a negative error value.
  * @retval #ML_ERROR_NONE Successful.
@@ -472,8 +473,7 @@ int ml_train_dataset_add_generator(ml_train_dataset_h dataset,
  * @retval #ML_ERROR_INVALID_PARAMETER Invalid parameter.
  */
 int ml_train_dataset_add_file(ml_train_dataset_h dataset,
-                              ml_train_dataset_data_usage_e usage,
-                              const char *file);
+                              ml_train_dataset_mode_e mode, const char *file);
 
 /**
  * @deprecated Deprecated since 6.5. Use ml_train_dataset_create() instead
@@ -517,8 +517,8 @@ int ml_train_dataset_destroy(ml_train_dataset_h dataset);
  * @details Use this function to set dataset property.
  * @since_tizen 6.0
  * @remarks the same property is applied over train, valid, testsets that are
- * added to the @a dataset, it is recommened to use @c
- * ml_train_dataset_set_property_for_usage() instead.
+ * added to the @a dataset, it is recommened to use @a
+ * ml_train_dataset_set_property_for_mode() instead.
  * @param[in] dataset The NNTrainer dataset handle.
  * @param[in]  ... Property values with NULL for termination.
  * @return @c 0 on success. Otherwise a negative error value.
@@ -531,19 +531,18 @@ int ml_train_dataset_set_property(ml_train_dataset_h dataset,
 
 /**
  * @brief Sets the neural network dataset property.
- * @details Use this function to set dataset property for a specific usage.
+ * @details Use this function to set dataset property for a specific mode.
  * @since_tizen 6.5
  * @param[in] dataset The NNTrainer dataset handle.
- * @param[in] usage The usage to set the property.
+ * @param[in] mode The mode to set the property.
  * @param[in]  ... Property values with NULL for termination.
  * @return @c 0 on success. Otherwise a negative error value.
  * @retval #ML_ERROR_NONE Successful.
  * @retval #ML_ERROR_NOT_SUPPORTED Not supported.
  * @retval #ML_ERROR_INVALID_PARAMETER Invalid parameter.
  */
-int ml_train_dataset_set_property_for_usage(ml_train_dataset_h dataset,
-                                            ml_train_dataset_data_usage_e usage,
-                                            ...);
+int ml_train_dataset_set_property_for_mode(ml_train_dataset_h dataset,
+                                           ml_train_dataset_mode_e mode, ...);
 
 /**
  * @brief Saves the model.

diff --git a/api/capi/src/nntrainer.cpp b/api/capi/src/nntrainer.cpp
index 0ed3eb9..fee9c7c 100644 (file)
--- a/api/capi/src/nntrainer.cpp
+++ b/api/capi/src/nntrainer.cpp
@@ -148,15 +148,15 @@ static int ml_train_dataset_create(ml_train_dataset_h *dataset,
 
   returnable f = [&]() {
     if (train != nullptr) {
-      nndataset->dataset[ML_TRAIN_DATASET_DATA_USAGE_TRAIN] =
+      nndataset->dataset[ML_TRAIN_DATASET_MODE_TRAIN] =
         ml::train::createDataset(type, train);
     }
     if (valid != nullptr) {
-      nndataset->dataset[ML_TRAIN_DATASET_DATA_USAGE_VALID] =
+      nndataset->dataset[ML_TRAIN_DATASET_MODE_VALID] =
         ml::train::createDataset(type, valid);
     }
     if (test != nullptr) {
-      nndataset->dataset[ML_TRAIN_DATASET_DATA_USAGE_TEST] =
+      nndataset->dataset[ML_TRAIN_DATASET_MODE_TEST] =
         ml::train::createDataset(type, test);
     }
     return ML_ERROR_NONE;
@@ -178,7 +178,7 @@ static int ml_train_dataset_create(ml_train_dataset_h *dataset,
  *
  * @tparam Args args needed to create the dataset
  * @param dataset dataset handle
- * @param usage target usage
+ * @param mode target mode
  * @param type dataset type
  * @param args args needed to create the dataset
  * @retval #ML_ERROR_NONE Successful
@@ -186,7 +186,7 @@ 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_data_usage_e usage,
+                                 ml_train_dataset_mode_e mode,
                                  ml::train::DatasetType type, Args &&... args) {
   check_feature_state();
   std::shared_ptr<ml::train::Dataset> underlying_dataset;
@@ -214,7 +214,7 @@ static int ml_train_dataset_add_(ml_train_dataset_h dataset,
     ML_TRAIN_GET_VALID_DATASET_LOCKED(nndataset, dataset);
     ML_TRAIN_ADOPT_LOCK(nndataset, dataset_lock);
 
-    nndataset->dataset[usage] = underlying_dataset;
+    nndataset->dataset[mode] = underlying_dataset;
   }
   return status;
 }
@@ -559,23 +559,20 @@ int ml_train_model_set_dataset(ml_train_model_h model,
   returnable f = [&]() {
     auto &[train_set, valid_set, test_set] = nndataset->dataset;
     int status = ML_ERROR_NONE;
-    status =
-      m->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN, train_set);
+    status = m->setDataset(ml::train::DatasetModeType::MODE_TRAIN, train_set);
     if (status != ML_ERROR_NONE) {
       return status;
     }
 
     if (valid_set != nullptr) {
-      status =
-        m->setDataset(ml::train::DatasetDataUsageType::DATA_VAL, valid_set);
+      status = m->setDataset(ml::train::DatasetModeType::MODE_VALID, valid_set);
       if (status != ML_ERROR_NONE) {
         return status;
       }
     }
 
     if (test_set != nullptr) {
-      status =
-        m->setDataset(ml::train::DatasetDataUsageType::DATA_TEST, test_set);
+      status = m->setDataset(ml::train::DatasetModeType::MODE_TEST, test_set);
       if (status != ML_ERROR_NONE) {
         return status;
       }
@@ -811,26 +808,25 @@ int ml_train_dataset_create(ml_train_dataset_h *dataset) {
 }
 
 int ml_train_dataset_add_generator(ml_train_dataset_h dataset,
-                                   ml_train_dataset_data_usage_e usage,
+                                   ml_train_dataset_mode_e mode,
                                    ml_train_datagen_cb cb, void *user_data) {
   check_feature_state();
   if (cb == nullptr) {
     return ML_ERROR_INVALID_PARAMETER;
   }
 
-  return ml_train_dataset_add_(
-    dataset, usage, ml::train::DatasetType::GENERATOR, cb, user_data);
+  return ml_train_dataset_add_(dataset, mode, ml::train::DatasetType::GENERATOR,
+                               cb, user_data);
 }
 
 int ml_train_dataset_add_file(ml_train_dataset_h dataset,
-                              ml_train_dataset_data_usage_e usage,
-                              const char *file) {
+                              ml_train_dataset_mode_e mode, const char *file) {
   check_feature_state();
   if (file == nullptr) {
     return ML_ERROR_INVALID_PARAMETER;
   }
 
-  return ml_train_dataset_add_(dataset, usage, ml::train::DatasetType::FILE,
+  return ml_train_dataset_add_(dataset, mode, ml::train::DatasetType::FILE,
                                file);
 }
 
@@ -857,21 +853,21 @@ int ml_train_dataset_create_with_file(ml_train_dataset_h *dataset,
 }
 
 /**
- * @brief set property for the specific data usage, main difference from @a
- * ml_train_dataset_set_property_for_usage() is that this function returns @a
+ * @brief set property for the specific data mode, main difference from @a
+ * ml_train_dataset_set_property_for_mode() is that this function returns @a
  * ML_ERROR_NOT_SUPPORTED if dataset does not exist.
  *
  * @param[in] dataset dataset
- * @param[in] usage usage
+ * @param[in] mode mode
  * @param[in] args argument
  * @retval #ML_ERROR_NONE successful
  * @retval #ML_ERROR_INVALID_PARAMETER when arg is invalid
  * @retval #ML_ERROR_NOT_SUPPORTED when dataset did not exist
  */
 static int
-ml_train_dataset_set_property_for_usage_(ml_train_dataset_h dataset,
-                                         ml_train_dataset_data_usage_e usage,
-                                         const std::vector<void *> &args) {
+ml_train_dataset_set_property_for_mode_(ml_train_dataset_h dataset,
+                                        ml_train_dataset_mode_e mode,
+                                        const std::vector<void *> &args) {
   int status = ML_ERROR_NONE;
   ml_train_dataset *nndataset;
 
@@ -883,7 +879,7 @@ ml_train_dataset_set_property_for_usage_(ml_train_dataset_h dataset,
     ML_TRAIN_GET_VALID_DATASET_LOCKED(nndataset, dataset);
     ML_TRAIN_ADOPT_LOCK(nndataset, dataset_lock);
 
-    auto &db = nndataset->dataset[usage];
+    auto &db = nndataset->dataset[mode];
 
     returnable f = [&db, &args]() {
       int status_ = ML_ERROR_NONE;
@@ -913,20 +909,20 @@ int ml_train_dataset_set_property(ml_train_dataset_h dataset, ...) {
   va_end(arguments);
 
   /// having status of ML_ERROR_NOT_SUPPORTED is not an error in this call.
-  int status = ml_train_dataset_set_property_for_usage_(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, arg_list);
+  int status = ml_train_dataset_set_property_for_mode_(
+    dataset, ML_TRAIN_DATASET_MODE_TRAIN, arg_list);
   if (status != ML_ERROR_NONE && status != ML_ERROR_NOT_SUPPORTED) {
     return status;
   }
 
-  status = ml_train_dataset_set_property_for_usage_(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_VALID, arg_list);
+  status = ml_train_dataset_set_property_for_mode_(
+    dataset, ML_TRAIN_DATASET_MODE_VALID, arg_list);
   if (status != ML_ERROR_NONE && status != ML_ERROR_NOT_SUPPORTED) {
     return status;
   }
 
-  status = ml_train_dataset_set_property_for_usage_(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TEST, arg_list);
+  status = ml_train_dataset_set_property_for_mode_(
+    dataset, ML_TRAIN_DATASET_MODE_TEST, arg_list);
   if (status != ML_ERROR_NONE && status != ML_ERROR_NOT_SUPPORTED) {
     return status;
   }
@@ -934,12 +930,11 @@ int ml_train_dataset_set_property(ml_train_dataset_h dataset, ...) {
   return ML_ERROR_NONE;
 }
 
-int ml_train_dataset_set_property_for_usage(ml_train_dataset_h dataset,
-                                            ml_train_dataset_data_usage_e usage,
-                                            ...) {
+int ml_train_dataset_set_property_for_mode(ml_train_dataset_h dataset,
+                                           ml_train_dataset_mode_e mode, ...) {
   std::vector<void *> arg_list;
   va_list arguments;
-  va_start(arguments, usage);
+  va_start(arguments, mode);
 
   void *data;
   while ((data = va_arg(arguments, void *))) {
@@ -947,8 +942,7 @@ int ml_train_dataset_set_property_for_usage(ml_train_dataset_h dataset,
   }
   va_end(arguments);
 
-  int status =
-    ml_train_dataset_set_property_for_usage_(dataset, usage, arg_list);
+  int status = ml_train_dataset_set_property_for_mode_(dataset, mode, arg_list);
 
   return status != ML_ERROR_NONE ? ML_ERROR_INVALID_PARAMETER : ML_ERROR_NONE;
 }

diff --git a/api/ccapi/include/dataset.h b/api/ccapi/include/dataset.h
index 8799cf3..b659bda 100644 (file)
--- a/api/ccapi/include/dataset.h
+++ b/api/ccapi/include/dataset.h
@@ -44,13 +44,13 @@ enum class DatasetType {
 };
 
 /**
- * @brief     Enumeration of data type
+ * @brief     Enumeration of data mode type
  */
-enum class DatasetDataUsageType {
-  DATA_TRAIN = ML_TRAIN_DATASET_DATA_USAGE_TRAIN, /** data for training */
-  DATA_VAL = ML_TRAIN_DATASET_DATA_USAGE_VALID,   /** data for validation */
-  DATA_TEST = ML_TRAIN_DATASET_DATA_USAGE_TEST,   /** data for test */
-  DATA_UNKNOWN                                    /** data not known */
+enum class DatasetModeType {
+  MODE_TRAIN = ML_TRAIN_DATASET_MODE_TRAIN, /** data for training */
+  MODE_VALID = ML_TRAIN_DATASET_MODE_VALID, /** data for validation */
+  MODE_TEST = ML_TRAIN_DATASET_MODE_TEST,   /** data for test */
+  MODE_UNKNOWN                              /** data not known */
 };
 
 /**

diff --git a/api/ccapi/include/model.h b/api/ccapi/include/model.h
index 26e30dd..63f5e7c 100644 (file)
--- a/api/ccapi/include/model.h
+++ b/api/ccapi/include/model.h
@@ -154,12 +154,12 @@ public:
 
   /**
    * @brief     Run Model train with callback function by user
-   * @param[in] usage usage of the dataset
+   * @param[in] mode mode of the dataset
    * @param[in] dataset set the dataset
    * @retval #ML_ERROR_NONE Successful.
    * @retval #ML_ERROR_INVALID_PARAMETER invalid parameter.
    */
-  virtual int setDataset(const ml::train::DatasetDataUsageType &usage,
+  virtual int setDataset(const ml::train::DatasetModeType &mode,
                          std::shared_ptr<Dataset> dataset) = 0;
 
   /**

diff --git a/api/nntrainer-api-common.h b/api/nntrainer-api-common.h
index 8240f66..f3ce2d3 100644 (file)
--- a/api/nntrainer-api-common.h
+++ b/api/nntrainer-api-common.h
@@ -177,13 +177,13 @@ typedef int (*ml_train_datagen_cb)(float **input, float **label, bool *last,
  * @since_tizen 6.5
  */
 typedef enum {
-  ML_TRAIN_DATASET_DATA_USAGE_TRAIN =
+  ML_TRAIN_DATASET_MODE_TRAIN =
     0, /**< The given data is for used when training */
-  ML_TRAIN_DATASET_DATA_USAGE_VALID =
+  ML_TRAIN_DATASET_MODE_VALID =
     1, /**< The given data is for used when validating */
-  ML_TRAIN_DATASET_DATA_USAGE_TEST =
+  ML_TRAIN_DATASET_MODE_TEST =
     2, /**< The given data is for used when testing */
-} ml_train_dataset_data_usage_e;
+} ml_train_dataset_mode_e;
 
 /**
  * @brief Enumeration for the neural network summary verbosity of NNTrainer.

diff --git a/nntrainer/dataset/databuffer.h b/nntrainer/dataset/databuffer.h
index 976feea..c72d198 100644 (file)
--- a/nntrainer/dataset/databuffer.h
+++ b/nntrainer/dataset/databuffer.h
@@ -109,7 +109,7 @@ public:
   /**
    * @brief     Display Progress
    * @param[in] count calculated set ( batch_size size )
-   * @param[in] type buffer type ( DATA_TRAIN, DATA_VAL, DATA_TEST )
+   * @param[in] type buffer type ( MODE_TRAIN, MODE_VALID, MODE_TEST )
    * @retval void
    */
   void displayProgress(const int count, float loss);

diff --git a/nntrainer/models/model_loader.cpp b/nntrainer/models/model_loader.cpp
index f68c2d5..6aaf4a4 100644 (file)
--- a/nntrainer/models/model_loader.cpp
+++ b/nntrainer/models/model_loader.cpp
@@ -210,8 +210,8 @@ int ModelLoader::loadDatasetConfigIni(dictionary *ini, NeuralNetwork &model) {
   std::string bufsizepros("buffer_size=");
   bufsizepros += iniparser_getstring(ini, "DataSet:BufferSize", "1");
 
-  std::function<int(const char *, DatasetDataUsageType, bool)> parse_and_set =
-    [&](const char *key, DatasetDataUsageType dt, bool required) -> int {
+  std::function<int(const char *, DatasetModeType, bool)> parse_and_set =
+    [&](const char *key, DatasetModeType dt, bool required) -> int {
     const char *path = iniparser_getstring(ini, key, NULL);
 
     if (path == NULL) {
@@ -231,13 +231,12 @@ int ModelLoader::loadDatasetConfigIni(dictionary *ini, NeuralNetwork &model) {
   };
 
   status =
-    parse_and_set("DataSet:TrainData", DatasetDataUsageType::DATA_TRAIN, true);
+    parse_and_set("DataSet:TrainData", DatasetModeType::MODE_TRAIN, true);
   NN_RETURN_STATUS();
   status =
-    parse_and_set("DataSet:ValidData", DatasetDataUsageType::DATA_VAL, false);
+    parse_and_set("DataSet:ValidData", DatasetModeType::MODE_VALID, false);
   NN_RETURN_STATUS();
-  status =
-    parse_and_set("DataSet:TestData", DatasetDataUsageType::DATA_TEST, false);
+  status = parse_and_set("DataSet:TestData", DatasetModeType::MODE_TEST, false);
   NN_RETURN_STATUS();
   const char *path = iniparser_getstring(ini, "Dataset:LabelData", NULL);
   if (path != NULL) {

diff --git a/nntrainer/models/neuralnet.cpp b/nntrainer/models/neuralnet.cpp
index cfa4394..7d7e236 100644 (file)
--- a/nntrainer/models/neuralnet.cpp
+++ b/nntrainer/models/neuralnet.cpp
@@ -540,9 +540,9 @@ std::vector<float *> NeuralNetwork::inference(std::vector<float *> &input) {
   return output;
 }
 
-int NeuralNetwork::setDataset(const DatasetDataUsageType &usage,
+int NeuralNetwork::setDataset(const DatasetModeType &mode,
                               std::shared_ptr<ml::train::Dataset> dataset) {
-  return setDataBuffer(usage, std::static_pointer_cast<DataBuffer>(dataset));
+  return setDataBuffer(mode, std::static_pointer_cast<DataBuffer>(dataset));
 }
 
 int NeuralNetwork::allocate(bool trainable) {
@@ -562,8 +562,7 @@ int NeuralNetwork::deallocate() {
 int NeuralNetwork::train(std::vector<std::string> values) {
   int status = ML_ERROR_NONE;
 
-  if (data_buffers[static_cast<int>(DatasetDataUsageType::DATA_TRAIN)] ==
-      nullptr) {
+  if (data_buffers[static_cast<int>(DatasetModeType::MODE_TRAIN)] == nullptr) {
     ml_loge("Cannot initialize the model without the train data buffer.");
     return ML_ERROR_INVALID_PARAMETER;
   }
@@ -753,13 +752,13 @@ int NeuralNetwork::setOptimizer(
   return ML_ERROR_NONE;
 }
 
-int NeuralNetwork::setDataBuffer(const DatasetDataUsageType &usage,
+int NeuralNetwork::setDataBuffer(const DatasetModeType &mode,
                                  std::shared_ptr<DataBuffer> data_buffer) {
   if (data_buffer == nullptr) {
     return ML_ERROR_INVALID_PARAMETER;
   }
 
-  this->data_buffers[static_cast<int>(usage)] = data_buffer;
+  this->data_buffers[static_cast<int>(mode)] = data_buffer;
 
   return ML_ERROR_NONE;
 }

diff --git a/nntrainer/models/neuralnet.h b/nntrainer/models/neuralnet.h
index e5cfbf9..5be8f12 100644 (file)
--- a/nntrainer/models/neuralnet.h
+++ b/nntrainer/models/neuralnet.h
@@ -47,7 +47,7 @@
 namespace ml::train {
 class DataSet;
 enum class DatasetType;
-enum class DatasetDataUsageType;
+enum class DatasetModeType;
 } // namespace ml::train
 
 namespace nntrainer {
@@ -59,7 +59,7 @@ using NetType = ml::train::ModelType;
 
 class DataBuffer;
 using DatasetType = ml::train::DatasetType;
-using DatasetDataUsageType = ml::train::DatasetDataUsageType;
+using DatasetModeType = ml::train::DatasetModeType;
 /**
  * @brief     Statistics from running or training a model
  */
@@ -293,22 +293,22 @@ public:
 
   /**
    * @brief     Run NeuralNetwork train with callback function by user
-   * @param[in] dt datatype (usage) where it should be
+   * @param[in] dt datatype (mode) where it should be
    * @param[in] dataset set the dataset
    * @retval #ML_ERROR_NONE Successful.
    * @retval #ML_ERROR_INVALID_PARAMETER invalid parameter.
    */
-  int setDataset(const DatasetDataUsageType &dt,
+  int setDataset(const DatasetModeType &dt,
                  std::shared_ptr<ml::train::Dataset> dataset);
 
   /**
    * @brief     Run NeuralNetwork train with callback function by user
-   * @param[in] dt datatype (usage) where it should be
+   * @param[in] dt datatype (mode) where it should be
    * @param[in] databuffer set the databuffer
    * @retval #ML_ERROR_NONE Successful.
    * @retval #ML_ERROR_INVALID_PARAMETER invalid parameter.
    */
-  int setDataBuffer(const DatasetDataUsageType &dt,
+  int setDataBuffer(const DatasetModeType &dt,
                     std::shared_ptr<DataBuffer> data_buffer);
 
   /**

diff --git a/test/ccapi/unittest_ccapi.cpp b/test/ccapi/unittest_ccapi.cpp
index e54a95b..61afb76 100644 (file)
--- a/test/ccapi/unittest_ccapi.cpp
+++ b/test/ccapi/unittest_ccapi.cpp
@@ -242,17 +242,15 @@ TEST(nntrainer_ccapi, train_dataset_with_file_01_p) {
     dataset = ml::train::createDataset(
       ml::train::DatasetType::FILE, getTestResPath("trainingSet.dat").c_str()));
   EXPECT_NO_THROW(dataset->setProperty({"buffer_size=100"}));
-  EXPECT_EQ(
-    model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN, dataset),
-    ML_ERROR_NONE);
+  EXPECT_EQ(model->setDataset(ml::train::DatasetModeType::MODE_TRAIN, dataset),
+            ML_ERROR_NONE);
 
   EXPECT_NO_THROW(
     dataset = ml::train::createDataset(ml::train::DatasetType::FILE,
                                        getTestResPath("valSet.dat").c_str()));
   EXPECT_NO_THROW(dataset->setProperty({"buffer_size=100"}));
-  EXPECT_EQ(
-    model->setDataset(ml::train::DatasetDataUsageType::DATA_VAL, dataset),
-    ML_ERROR_NONE);
+  EXPECT_EQ(model->setDataset(ml::train::DatasetModeType::MODE_VALID, dataset),
+            ML_ERROR_NONE);
 
   EXPECT_EQ(model->setProperty({"loss=cross", "batch_size=16", "epochs=2",
                                 "save_path=model.bin"}),
@@ -297,16 +295,14 @@ TEST(nntrainer_ccapi, train_dataset_with_generator_01_p) {
   EXPECT_NO_THROW(dataset = ml::train::createDataset(
                     ml::train::DatasetType::GENERATOR, getBatch_train));
   EXPECT_NO_THROW(dataset->setProperty({"buffer_size=100"}));
-  EXPECT_EQ(
-    model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN, dataset),
-    ML_ERROR_NONE);
+  EXPECT_EQ(model->setDataset(ml::train::DatasetModeType::MODE_TRAIN, dataset),
+            ML_ERROR_NONE);
 
   EXPECT_NO_THROW(dataset = ml::train::createDataset(
                     ml::train::DatasetType::GENERATOR, getBatch_val));
   EXPECT_NO_THROW(dataset->setProperty({"buffer_size=100"}));
-  EXPECT_EQ(
-    model->setDataset(ml::train::DatasetDataUsageType::DATA_VAL, dataset),
-    ML_ERROR_NONE);
+  EXPECT_EQ(model->setDataset(ml::train::DatasetModeType::MODE_VALID, dataset),
+            ML_ERROR_NONE);
 
   EXPECT_EQ(model->setProperty({"loss=cross", "batch_size=16", "epochs=2",
                                 "save_path=model.bin"}),
@@ -352,17 +348,15 @@ TEST(nntrainer_ccapi, train_batch_size_update_after) {
     dataset = ml::train::createDataset(
       ml::train::DatasetType::FILE, getTestResPath("trainingSet.dat").c_str()));
   EXPECT_NO_THROW(dataset->setProperty({"buffer_size=100"}));
-  EXPECT_EQ(
-    model->setDataset(ml::train::DatasetDataUsageType::DATA_TRAIN, dataset),
-    ML_ERROR_NONE);
+  EXPECT_EQ(model->setDataset(ml::train::DatasetModeType::MODE_TRAIN, dataset),
+            ML_ERROR_NONE);
 
   EXPECT_NO_THROW(
     dataset = ml::train::createDataset(ml::train::DatasetType::FILE,
                                        getTestResPath("valSet.dat").c_str()));
   EXPECT_NO_THROW(dataset->setProperty({"buffer_size=100"}));
-  EXPECT_EQ(
-    model->setDataset(ml::train::DatasetDataUsageType::DATA_VAL, dataset),
-    ML_ERROR_NONE);
+  EXPECT_EQ(model->setDataset(ml::train::DatasetModeType::MODE_VALID, dataset),
+            ML_ERROR_NONE);
 
   EXPECT_EQ(model->setProperty({"loss=cross", "batch_size=16", "epochs=1"}),
             ML_ERROR_NONE);

diff --git a/test/tizen_capi/unittest_tizen_capi_dataset.cpp b/test/tizen_capi/unittest_tizen_capi_dataset.cpp
index b4349cd..8821e02 100644 (file)
--- a/test/tizen_capi/unittest_tizen_capi_dataset.cpp
+++ b/test/tizen_capi/unittest_tizen_capi_dataset.cpp
@@ -146,20 +146,20 @@ TEST(nntrainer_cpi_dataset, add_generator_01_p) {
   int status = ml_train_dataset_create(&dataset);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_generator(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, getBatch_train, NULL);
+  status = ml_train_dataset_add_generator(dataset, ML_TRAIN_DATASET_MODE_TRAIN,
+                                          getBatch_train, NULL);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_generator(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, getBatch_val, NULL);
+  status = ml_train_dataset_add_generator(dataset, ML_TRAIN_DATASET_MODE_TRAIN,
+                                          getBatch_val, NULL);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_generator(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_VALID, getBatch_train, NULL);
+  status = ml_train_dataset_add_generator(dataset, ML_TRAIN_DATASET_MODE_VALID,
+                                          getBatch_train, NULL);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_generator(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TEST, getBatch_train, NULL);
+  status = ml_train_dataset_add_generator(dataset, ML_TRAIN_DATASET_MODE_TEST,
+                                          getBatch_train, NULL);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
   status = ml_train_dataset_destroy(dataset);
@@ -174,8 +174,8 @@ TEST(nntrainer_cpi_dataset, add_generator_null_callback_n) {
   int status = ml_train_dataset_create(&dataset);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_generator(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, NULL, NULL);
+  status = ml_train_dataset_add_generator(dataset, ML_TRAIN_DATASET_MODE_TRAIN,
+                                          NULL, NULL);
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 
   status = ml_train_dataset_destroy(dataset);
@@ -186,8 +186,8 @@ TEST(nntrainer_cpi_dataset, add_generator_null_callback_n) {
  * @brief Neural Network Add Generator Test (negative test)
  */
 TEST(nntrainer_cpi_dataset, add_generator_null_handle_n) {
-  int status = ml_train_dataset_add_generator(
-    NULL, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, NULL, NULL);
+  int status = ml_train_dataset_add_generator(NULL, ML_TRAIN_DATASET_MODE_TRAIN,
+                                              NULL, NULL);
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 }
 
@@ -199,19 +199,19 @@ TEST(nntrainer_cpi_dataset, add_file_01_p) {
   int status = ml_train_dataset_create(&dataset);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN,
+  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_MODE_TRAIN,
                                      getTestResPath("trainingSet.dat").c_str());
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN,
+  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_MODE_TRAIN,
                                      getTestResPath("valSet.dat").c_str());
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_DATA_USAGE_VALID,
+  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_MODE_VALID,
                                      getTestResPath("trainingSet.dat").c_str());
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_DATA_USAGE_TEST,
+  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_MODE_TEST,
                                      getTestResPath("trainingSet.dat").c_str());
   EXPECT_EQ(status, ML_ERROR_NONE);
 
@@ -228,7 +228,7 @@ TEST(nntrainer_cpi_dataset, add_file_null_callback_n) {
   EXPECT_EQ(status, ML_ERROR_NONE);
 
   status =
-    ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, NULL);
+    ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_MODE_TRAIN, NULL);
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 
   status = ml_train_dataset_destroy(dataset);
@@ -243,7 +243,7 @@ TEST(nntrainer_cpi_dataset, add_file_does_not_exist_n) {
   int status = ml_train_dataset_create(&dataset);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN,
+  status = ml_train_dataset_add_file(dataset, ML_TRAIN_DATASET_MODE_TRAIN,
                                      "./not_existing_file");
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 
@@ -256,7 +256,7 @@ TEST(nntrainer_cpi_dataset, add_file_does_not_exist_n) {
  */
 TEST(nntrainer_cpi_dataset, add_file_null_handle_n) {
   int status =
-    ml_train_dataset_add_file(NULL, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, NULL);
+    ml_train_dataset_add_file(NULL, ML_TRAIN_DATASET_MODE_TRAIN, NULL);
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 }
 
@@ -339,33 +339,33 @@ TEST(nntrainer_capi_dataset, set_dataset_property_04_p) {
 /**
  * @brief Neural Network Dataset set Property Test (positive test)
  */
-TEST(nntrainer_capi_dataset, set_dataset_property_for_usage_01_p) {
+TEST(nntrainer_capi_dataset, set_dataset_property_for_mode_01_p) {
   ml_train_dataset_h dataset;
   int status = ML_ERROR_NONE;
 
   status = ml_train_dataset_create(&dataset);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_set_property_for_usage(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, "buffer_size=1", NULL);
+  status = ml_train_dataset_set_property_for_mode(
+    dataset, ML_TRAIN_DATASET_MODE_TRAIN, "buffer_size=1", NULL);
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 
-  status = ml_train_dataset_add_generator(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, getBatch_val, nullptr);
-  status = ml_train_dataset_set_property_for_usage(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TRAIN, "buffer_size=1", NULL);
+  status = ml_train_dataset_add_generator(dataset, ML_TRAIN_DATASET_MODE_TRAIN,
+                                          getBatch_val, nullptr);
+  status = ml_train_dataset_set_property_for_mode(
+    dataset, ML_TRAIN_DATASET_MODE_TRAIN, "buffer_size=1", NULL);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_generator(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_VALID, getBatch_val, nullptr);
-  status = ml_train_dataset_set_property_for_usage(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_VALID, "buffer_size=1", NULL);
+  status = ml_train_dataset_add_generator(dataset, ML_TRAIN_DATASET_MODE_VALID,
+                                          getBatch_val, nullptr);
+  status = ml_train_dataset_set_property_for_mode(
+    dataset, ML_TRAIN_DATASET_MODE_VALID, "buffer_size=1", NULL);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_add_generator(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TEST, getBatch_val, nullptr);
-  status = ml_train_dataset_set_property_for_usage(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TEST, "buffer_size=1", NULL);
+  status = ml_train_dataset_add_generator(dataset, ML_TRAIN_DATASET_MODE_TEST,
+                                          getBatch_val, nullptr);
+  status = ml_train_dataset_set_property_for_mode(
+    dataset, ML_TRAIN_DATASET_MODE_TEST, "buffer_size=1", NULL);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
   status = ml_train_dataset_destroy(dataset);
@@ -376,7 +376,7 @@ TEST(nntrainer_capi_dataset, set_dataset_property_for_usage_01_p) {
  * @brief Neural Network Dataset set Property Test (negative test)
  */
 TEST(nntrainer_capi_dataset,
-     set_dataset_property_for_usage_does_not_exist_valid_n) {
+     set_dataset_property_for_mode_does_not_exist_valid_n) {
   ml_train_dataset_h dataset;
   int status = ML_ERROR_NONE;
 
@@ -384,8 +384,8 @@ TEST(nntrainer_capi_dataset,
                                                   nullptr, getBatch_train);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_set_property_for_usage(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_VALID, "buffer_size=1", NULL);
+  status = ml_train_dataset_set_property_for_mode(
+    dataset, ML_TRAIN_DATASET_MODE_VALID, "buffer_size=1", NULL);
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 
   status = ml_train_dataset_destroy(dataset);
@@ -396,7 +396,7 @@ TEST(nntrainer_capi_dataset,
  * @brief Neural Network Dataset set Property Test (negative test)
  */
 TEST(nntrainer_capi_dataset,
-     set_dataset_property_for_usage_does_not_exist_test_n) {
+     set_dataset_property_for_mode_does_not_exist_test_n) {
   ml_train_dataset_h dataset;
   int status = ML_ERROR_NONE;
 
@@ -404,8 +404,8 @@ TEST(nntrainer_capi_dataset,
                                                   nullptr, nullptr);
   EXPECT_EQ(status, ML_ERROR_NONE);
 
-  status = ml_train_dataset_set_property_for_usage(
-    dataset, ML_TRAIN_DATASET_DATA_USAGE_TEST, "buffer_size=1", NULL);
+  status = ml_train_dataset_set_property_for_mode(
+    dataset, ML_TRAIN_DATASET_MODE_TEST, "buffer_size=1", NULL);
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 
   status = ml_train_dataset_destroy(dataset);