- Added learning rate scheduler create/destroy/set property/set property with single param api
- Added set learning rate scheduler to optimizer
- Added ml_train_lr_scheduler_type_e enum
- Fix some comments
Signed-off-by: hyeonseok lee <hs89.lee@samsung.com>
*/
/**
- * @brief A handle of an NNTrainer model.
+ * @brief A handle of a NNTrainer model.
* @since_tizen 6.0
*/
typedef void *ml_train_model_h;
/**
- * @brief A handle of an NNTrainer layer.
+ * @brief A handle of a NNTrainer layer.
* @since_tizen 6.0
*/
typedef void *ml_train_layer_h;
/**
- * @brief A handle of an NNTrainer optimizer.
+ * @brief A handle of a NNTrainer optimizer.
* @since_tizen 6.0
*/
typedef void *ml_train_optimizer_h;
/**
- * @brief A handle of an NNTrainer dataset.
+ * @brief A handle of a NNTrainer learning rate scheduler.
+ * @since_tizen 7.5
+ */
+typedef void *ml_train_lr_scheduler_h;
+
+/**
+ * @brief A handle of a NNTrainer dataset.
* @since_tizen 6.0
*/
typedef void *ml_train_dataset_h;
int ml_train_optimizer_set_property(ml_train_optimizer_h optimizer, ...);
/**
+ * @brief Sets the learning rate scheduler for the optimizer.
+ * @details Use this function to set learning rate scheduler. This transfers
+ * the ownership of the scheduler to the optimizer. No need to destroy the
+ * optimizer if it is to a model.
+ * @since_tizen 7.5
+ * @remarks Unsets the previously set lr_scheduler, if any. The previously set
+ * lr_scheduler must be freed using ml_train_lr_scheduler_destroy().
+ * @param[in] optimizer The NNTrainer optimizer handle.
+ * @param[in] lr_scheduler The NNTrainer lr scheduler handle.
+ * @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_optimizer_set_lr_scheduler(ml_train_optimizer_h optimizer,
+ ml_train_lr_scheduler_h lr_scheduler);
+
+/**
+ * @brief Creates a learning rate scheduler for optimizer.
+ * @details Use this function to create learning rate scheduler for optimizer.
+ * If not set to a optimizer, @a lr_sheduler should be released using
+ * ml_train_lr_scheduler_destroy(). If set to a optimizer, @a lr_scheduler is
+ * available until optimizer is released.
+ * @since_tizen 7.5
+ * @remarks If the function succeeds, @a lr_scheduler must be released using
+ * ml_train_lr_scheduler_destroy(), if not set to a optimizer. If set to a
+ * optimizer, @a lr_scheduler is available until the optimizer is released.
+ * @param[out] lr_scheduler The NNTrainer learning rate scheduler handle.
+ * @param[in] type The NNTrainer learning rate scheduler type.
+ * @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_lr_scheduler_create(ml_train_lr_scheduler_h *lr_scheduler,
+ ml_train_lr_scheduler_type_e type);
+
+/**
+ * @brief Frees the learning rate scheduler.
+ * @details Use this function to destroy learning rate scheduler. Fails if
+ * learning rate scheduler is owned by a optimizer.
+ * @since_tizen 7.5
+ * @param[in] lr_scheduler The NNTrainer learning rate scheduler handle.
+ * @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_lr_scheduler_destroy(ml_train_lr_scheduler_h lr_scheduler);
+
+/**
+ * @brief Sets the learning rate scheduler property.
+ * @details Use this function to set learning rate scheduler property.
+ * @since_tizen 7.5
+ * @param[in] lr_scheduler The NNTrainer learning rate scheduler handle.
+ * @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_lr_scheduler_set_property(ml_train_lr_scheduler_h lr_scheduler,
+ ...);
+
+/**
* @deprecated Deprecated since 6.5. Use ml_train_dataset_create() instead.
* @brief Creates a dataset with generators to feed to a neural network.
* @details Use this function to create a neural network dataset using
} ml_train_layer;
/**
+ * @brief Struct to wrap learning rate scheduler for the API
+ * @note optimizer mutex must be locked before learning rate scheduler lock, if
+ * optimizer lock is needed
+ */
+typedef struct {
+ uint magic;
+ std::shared_ptr<ml::train::LearningRateScheduler> lr_scheduler;
+ bool in_use;
+ std::mutex m;
+} ml_train_lr_scheduler;
+
+/**
* @brief Struct to wrap neural network optimizer for the API
* @note model mutex must be locked before optimizer lock, if model lock is
* needed
typedef struct {
uint magic;
std::shared_ptr<ml::train::Optimizer> optimizer;
+ ml_train_lr_scheduler *lr_sheduler;
bool in_use;
std::mutex m;
} ml_train_optimizer;
} while (0)
/**
- * @brief Check validity of the user passed arguments and lock the object
+ * @brief Check validity of the user passed arguments
*/
#define ML_TRAIN_GET_VALID_HANDLE(obj, obj_h, obj_type, obj_name) \
do { \
} while (0)
/**
- * @brief Check validity of the user passed arguments and lock the object
+ * @brief Check validity of the user passed arguments, reset magic if in use
+ * and lock the object
*/
#define ML_TRAIN_GET_VALID_HANDLE_LOCKED_RESET(obj, obj_h, obj_type, obj_name) \
do { \
} while (0)
/**
- * @brief Check validity of the user passed arguments and lock the object
+ * @brief Reset object magic
*/
#define ML_TRAIN_RESET_VALIDATED_HANDLE(obj) \
do { \
"optimizer")
/**
+ * @brief Check validity of passed lr_scheduler and lock the object
+ */
+#define ML_TRAIN_GET_VALID_LR_SCHEDULER_LOCKED(nnlrscheduler, lrscheduler) \
+ ML_TRAIN_GET_VALID_HANDLE_LOCKED(nnlrscheduler, lrscheduler, \
+ ml_train_lr_scheduler, "lr_scheduler")
+
+/**
+ * @brief Check validity of passed lr_scheduler, reset magic and lock the
+ * object
+ */
+#define ML_TRAIN_GET_VALID_LR_SCHEDULER_LOCKED_RESET(nnlrscheduler, \
+ lrscheduler) \
+ ML_TRAIN_GET_VALID_HANDLE_LOCKED_RESET( \
+ nnlrscheduler, lrscheduler, ml_train_lr_scheduler, "lr_scheduler")
+
+/**
* @brief Check validity of passed dataset and lock the object
*/
#define ML_TRAIN_GET_VALID_DATASET_LOCKED(nndataset, dataset) \
ml_train_optimizer_h optimizer, const char *single_param);
/**
+ * @brief Sets the learning rate scheduler property with single param.
+ * @details Use this function to set learning rate scheduler property.
+ * @since_tizen 7.5
+ * API to solve va_list issue of Dllimport of C# interop.
+ * The input format of single_param must be 'key = value' format, and it
+ * received as shown in the example below. delimiter is '|'. e.g)
+ * ml_train_lr_scheduler_set_property_with_single_param(lr_scheduler,
+ * "learning_rate=0.01 | decay_rate=0.5 | decay_steps=1000");
+ * @param[in] lr_scheduler The learning rate scheduler handle.
+ * @param[in] single_param Property values.
+ * @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_lr_scheduler_set_property_with_single_param(
+ ml_train_lr_scheduler_h lr_scheduler, const char *single_param);
+
+/**
* @brief Sets the neural network dataset property with single param.
* @details Use this function to set dataset property for a specific mode.
* API to solve va_list issue of Dllimport of C# interop.
ML_TRAIN_ADOPT_LOCK(nnmodel, model_lock);
}
- std::shared_ptr<ml::train::Model> m;
- m = nnmodel->model;
-
if (nnmodel->optimizer) {
+ if (nnmodel->optimizer->lr_sheduler) {
+ ML_TRAIN_RESET_VALIDATED_HANDLE(nnmodel->optimizer->lr_sheduler);
+ delete nnmodel->optimizer->lr_sheduler;
+ }
+
ML_TRAIN_RESET_VALIDATED_HANDLE(nnmodel->optimizer);
delete nnmodel->optimizer;
}
status = nntrainer_exception_boundary(f);
if (status == ML_ERROR_NONE) {
nnopt->in_use = true;
- if (nnmodel->optimizer)
+ if (nnmodel->optimizer) {
nnmodel->optimizer->in_use = false;
+ }
nnmodel->optimizer = nnopt;
}
ml_train_optimizer *nnopt = new ml_train_optimizer;
nnopt->magic = ML_NNTRAINER_MAGIC;
nnopt->in_use = false;
+ nnopt->lr_sheduler = NULL;
returnable f = [&]() {
nnopt->optimizer =
"Delete model will delete this optimizer.");
return ML_ERROR_INVALID_PARAMETER;
}
+
+ if (nnopt->lr_sheduler) {
+ ML_TRAIN_RESET_VALIDATED_HANDLE(nnopt->lr_sheduler);
+ delete nnopt->lr_sheduler;
+ }
}
delete nnopt;
return ml_train_optimizer_set_property(optimizer, single_param, NULL);
}
+int ml_train_optimizer_set_lr_scheduler(ml_train_optimizer_h optimizer,
+ ml_train_lr_scheduler_h lr_scheduler) {
+ int status = ML_ERROR_NONE;
+ ml_train_optimizer *nnopt;
+ ml_train_lr_scheduler *nnlrscheduler;
+
+ check_feature_state();
+
+ ML_TRAIN_GET_VALID_OPT_LOCKED(nnopt, optimizer);
+ ML_TRAIN_ADOPT_LOCK(nnopt, opt_lock);
+ ML_TRAIN_GET_VALID_LR_SCHEDULER_LOCKED(nnlrscheduler, lr_scheduler);
+ ML_TRAIN_ADOPT_LOCK(nnlrscheduler, lr_scheduler_lock);
+
+ if (nnlrscheduler->in_use) {
+ ml_loge("learning rate scheduler already in use.");
+ return ML_ERROR_INVALID_PARAMETER;
+ }
+
+ std::shared_ptr<ml::train::Optimizer> opt;
+ std::shared_ptr<ml::train::LearningRateScheduler> lr_sched;
+
+ opt = nnopt->optimizer;
+ lr_sched = nnlrscheduler->lr_scheduler;
+
+ returnable f = [&]() { return opt->setLearningRateScheduler(lr_sched); };
+
+ status = nntrainer_exception_boundary(f);
+ if (status == ML_ERROR_NONE) {
+ nnlrscheduler->in_use = true;
+ if (nnopt->lr_sheduler) {
+ nnopt->lr_sheduler->in_use = false;
+ }
+ nnopt->lr_sheduler = nnlrscheduler;
+ }
+
+ return status;
+}
+
+int ml_train_lr_scheduler_create(ml_train_lr_scheduler_h *lr_scheduler,
+ ml_train_lr_scheduler_type_e type) {
+ int status = ML_ERROR_NONE;
+
+ check_feature_state();
+
+ ml_train_lr_scheduler *nnlrscheduler = new ml_train_lr_scheduler;
+ nnlrscheduler->magic = ML_NNTRAINER_MAGIC;
+ nnlrscheduler->in_use = false;
+
+ returnable f = [&]() {
+ nnlrscheduler->lr_scheduler = ml::train::createLearningRateScheduler(
+ (ml::train::LearningRateSchedulerType)type);
+ return ML_ERROR_NONE;
+ };
+
+ status = nntrainer_exception_boundary(f);
+ if (status != ML_ERROR_NONE) {
+ delete nnlrscheduler;
+ ml_loge("creating optimizer failed");
+ } else {
+ *lr_scheduler = nnlrscheduler;
+ }
+
+ return status;
+}
+
+int ml_train_lr_scheduler_destroy(ml_train_lr_scheduler_h lr_scheduler) {
+ int status = ML_ERROR_NONE;
+ ml_train_lr_scheduler *nnlrscheduler;
+
+ check_feature_state();
+
+ {
+ ML_TRAIN_GET_VALID_LR_SCHEDULER_LOCKED_RESET(nnlrscheduler, lr_scheduler);
+ ML_TRAIN_ADOPT_LOCK(nnlrscheduler, lr_scheduler_lock);
+
+ if (nnlrscheduler->in_use) {
+ ml_loge(
+ "Cannot delete learning rate scheduler already set to a optimizer."
+ "Delete optimizer will delete this learning rate scheduler.");
+ return ML_ERROR_INVALID_PARAMETER;
+ }
+ }
+
+ delete nnlrscheduler;
+ return status;
+}
+
+int ml_train_lr_scheduler_set_property(ml_train_lr_scheduler_h lr_scheduler,
+ ...) {
+ int status = ML_ERROR_NONE;
+ ml_train_lr_scheduler *nnlrscheduler;
+ const char *data;
+ std::shared_ptr<ml::train::LearningRateScheduler> lr_sched;
+
+ check_feature_state();
+
+ ML_TRAIN_VERIFY_VALID_HANDLE(lr_scheduler);
+
+ std::vector<std::string> arg_list;
+ va_list arguments;
+ va_start(arguments, lr_scheduler);
+
+ while ((data = va_arg(arguments, const char *))) {
+ arg_list.push_back(data);
+ }
+
+ va_end(arguments);
+
+ {
+ ML_TRAIN_GET_VALID_LR_SCHEDULER_LOCKED(nnlrscheduler, lr_scheduler);
+ ML_TRAIN_ADOPT_LOCK(nnlrscheduler, lr_scheduler_lock);
+
+ lr_sched = nnlrscheduler->lr_scheduler;
+ }
+
+ returnable f = [&]() {
+ lr_sched->setProperty(arg_list);
+ return ML_ERROR_NONE;
+ };
+
+ status = nntrainer_exception_boundary(f);
+
+ return status;
+}
+
+int ml_train_lr_scheduler_set_property_with_single_param(
+ ml_train_lr_scheduler_h lr_scheduler, const char *single_param) {
+ ML_TRAIN_VERIFY_VALID_HANDLE(lr_scheduler);
+
+ return ml_train_lr_scheduler_set_property(lr_scheduler, single_param, NULL);
+}
+
int ml_train_dataset_create(ml_train_dataset_h *dataset) {
return ml_train_dataset_create(dataset, ml::train::DatasetType::UNKNOWN,
nullptr, nullptr, nullptr);
* @brief Enumeration of learning rate scheduler type
*/
enum LearningRateSchedulerType {
- CONSTANT = 0, /**< constant */
- EXPONENTIAL, /**< exponentially decay */
- STEP /**< step wise decay */
+ CONSTANT = ML_TRAIN_LR_SCHEDULER_TYPE_CONSTANT, /**< constant */
+ EXPONENTIAL =
+ ML_TRAIN_LR_SCHEDULER_TYPE_EXPONENTIAL, /**< exponentially decay */
+ STEP = ML_TRAIN_LR_SCHEDULER_TYPE_STEP /**< step wise decay */
};
/**
} ml_train_optimizer_type_e;
/**
+ * @brief Enumeration for the learning rate scheduler type of NNTrainer.
+ * @since_tizen 7.5
+ */
+typedef enum {
+ ML_TRAIN_LR_SCHEDULER_TYPE_CONSTANT = 0, /**< Constant lr scheduler */
+ ML_TRAIN_LR_SCHEDULER_TYPE_EXPONENTIAL = 1, /**< Exponentially lr scheduler */
+ ML_TRAIN_LR_SCHEDULER_TYPE_STEP = 2, /**< Step lr scheduler */
+ ML_TRAIN_LR_SCHEDULER_TYPE_UNKNOWN = 999 /**< Unknown lr scheduler */
+} ml_train_lr_scheduler_type_e;
+
+/**
* @brief Dataset generator callback function for train/valid/test data.
*
* @details The user of the API must provide this callback function to supply
projects / platform / core / ml / nntrainer.git / commitdiff