[Trivial] Fix Typo

Fix Typo
- model_loader.h
- model_loader.cpp
- dynamic_training_optimization.h
- dynamic_training_optimization.cpp
- tensor_trainer_nntrainer.hh
- tensor_trainer_nntrainer.cc

Signed-off-by: Donghyeon Jeong <dhyeon.jeong@samsung.com>

diff --git a/nnstreamer/tensor_trainer/tensor_trainer_nntrainer.cc b/nnstreamer/tensor_trainer/tensor_trainer_nntrainer.cc
index d8bb327..fb8c04d 100644 (file)
--- a/nnstreamer/tensor_trainer/tensor_trainer_nntrainer.cc
+++ b/nnstreamer/tensor_trainer/tensor_trainer_nntrainer.cc
@@ -1,12 +1,12 @@
 /* SPDX-License-Identifier: Apache-2.0 */
 /**
- * NNStreamer tensor_trainer subplugin for nntrainer
+ * NNStreamer tensor_trainer sub-plugin for nntrainer
  * Copyright (C) 2022 Hyunil Park <hyunil46.park@samsung.com>
  */
 /**
  * @file   tensor_trainer_nntrainer.cc
  * @date   02 Dec 2022
- * @brief  NNStreamer tensor_trainer subplugin
+ * @brief  NNStreamer tensor_trainer sub-plugin
  * @see    http://github.com/nnstreamer/nntrainer
  * @see    http://github.com/nnstreamer/nnstreamer
  * @author Hyunil Park <hyunil46.park@samsung.com>
@@ -15,7 +15,7 @@
 
 /**
  * # Action description and constraints
- * tensor_trainer_nntrainer.cc is a nnstreamer sub-plugine be used by
+ * tensor_trainer_nntrainer.cc is a nnstreamer sub-plugin be used by
  * nnstreamer(tensor_trainer) for training model.
  *
  * ## Notice
@@ -24,7 +24,7 @@
  * 3. The current feature behavior has been tested with MNIST.
  * 4. mnist.json has 'gst_caps' containing the information below.
  * "gst_caps":"other/tensors, format=(string)static, framerate=(fraction)0/1,
- *  num_tensors=(int)2, dimensions=(string)1:1:784:1.1:1:10:1,
+ *  num_tensors=(int)2, dimensions=(string)1:1:784:1,1:1:10:1,
  *  types=(string)float32.float32"
  *
  * ## Example launch line is as below
@@ -55,11 +55,11 @@
  *   multiple inputs and labels in tensors(in case of MNIST, all is 1), set how
  *   many samples are taken for validation model.
  * 7. epochs: epochs are repetitions of training samples and validation
- *   smaples. number of samples received for model training is
+ *   samples. number of samples received for model training is
  *   (num-training-samples + num-validation-samples) * epochs
  *
  * ## Action
- * When a subplugin is loaded at runtime,
+ * When a sub-plugin is loaded at runtime,
  * NNTrainerTrain's num_inputs, num_labels, num_training_samples,
  * num_validation_samples, model_save_path and num_epochs is set by
  * getNNStreamerProperties() and each tensor size is set.
@@ -116,7 +116,7 @@ void nntrainer_thread_func(NNTrainer::NNTrainerTrain *nntrainer) {
 /**
  * @brief push_data function
  * tensor_trainer call this function to push tensor data.
- * For epoch, (number of trin samples + number of valid samples) * epoch
+ * For epoch, (number of train samples + number of valid samples) * epoch
  * data should be received.
  * Sub-plugin don't keep dataset for epoch.
  */
@@ -217,10 +217,10 @@ void NNTrainer::InputTensorsInfo::getSample(float **input, float **label,
   pid_t pid = getpid();
   pid_t tid = syscall(SYS_gettid);
 
-  /* After the epoch ends, the subplugin has no data yet to send. */
+  /* After the epoch ends, the sub-plugin has no data yet to send. */
   if (push_count == 0) {
     ml_logd("locked, need to wait for more data, "
-            "After the epoch ends, the subplugin has no data yet to send.");
+            "After the epoch ends, the sub-plugin has no data yet to send.");
     std::unique_lock<std::mutex> lock(data_wait_lock);
     is_data_wait_locked = TRUE;
     data_wait.wait(lock);

diff --git a/nnstreamer/tensor_trainer/tensor_trainer_nntrainer.hh b/nnstreamer/tensor_trainer/tensor_trainer_nntrainer.hh
index 65290e1..c672942 100644 (file)
--- a/nnstreamer/tensor_trainer/tensor_trainer_nntrainer.hh
+++ b/nnstreamer/tensor_trainer/tensor_trainer_nntrainer.hh
@@ -32,13 +32,13 @@ struct TensorData {
 class InputTensorsInfo;
 
 /**
- * @brief NNTrainer interface for nnstreamer trainer subplugin
+ * @brief NNTrainer interface for nnstreamer trainer sub-plugin
  */
 class NNTrainerTrain {
 public:
   /**
    * @brief Construct a new NNTrainerTrain object
-   * @param prop tensor trainer subplugin properties
+   * @param prop tensor trainer sub-plugin properties
    * @param _model_config model configuration file path
    */
   NNTrainerTrain(const GstTensorTrainerProperties *prop,
@@ -73,7 +73,7 @@ public:
 
   /**
    * @brief Get NNStreamer tensor_trainer properties
-   * @param prop Tensor trainer subplugin properties
+   * @param prop Tensor trainer sub-plugin properties
    */
   void getNNStreamerProperties(const GstTensorTrainerProperties *prop);
 

diff --git a/nntrainer/models/dynamic_training_optimization.cpp b/nntrainer/models/dynamic_training_optimization.cpp
index 72eda00..569fe6b 100644 (file)
--- a/nntrainer/models/dynamic_training_optimization.cpp
+++ b/nntrainer/models/dynamic_training_optimization.cpp
@@ -107,7 +107,7 @@ bool DynamicTrainingOptimization::checkIfApply(float reduced_ratio,
                                                float learning_rate) {
   /**
    * If the reduced update ratio is higher than 1, then always apply update.
-   * If the reduced update raito is less than 1, then apply it with
+   * If the reduced update ratio is less than 1, then apply it with
    * probability = update ratio
    */
   if (dist(rng) < reduced_ratio * learning_rate / threshold)
@@ -126,7 +126,7 @@ float DynamicTrainingOptimization::reduceByMax(Tensor const &ratio) {
 
 /**
  * @brief     Operation to decide if update should be skipped
- * @note      Calcalate l2 norm of the tensor averaged by its size
+ * @note      Calculate l2 norm of the tensor averaged by its size
  */
 float DynamicTrainingOptimization::reduceByNorm(Tensor const &ratio) {
   float l2norm = ratio.l2norm();

diff --git a/nntrainer/models/dynamic_training_optimization.h b/nntrainer/models/dynamic_training_optimization.h
index 210c368..8e237e1 100644 (file)
--- a/nntrainer/models/dynamic_training_optimization.h
+++ b/nntrainer/models/dynamic_training_optimization.h
@@ -27,7 +27,7 @@
  * There are two supported reduction operations which reduce the gradient and
  * the weight to a single value in order to compare it with a threshold.
  * If the reduced value is less than threshold, the update is performed with
- * some probabilty proportional to the value. If the reduced value is higher
+ * some probability proportional to the value. If the reduced value is higher
  * than threshold, then the update is always performed.
  *
  */
@@ -229,7 +229,7 @@ private:
 
   /**
    * @brief     Operation to decide if update should be skipped
-   * @note      Calcalate l2 norm of the tensor averaged by its size
+   * @note      Calculate l2 norm of the tensor averaged by its size
    */
   static float reduceByNorm(Tensor const &ratio);
 };

diff --git a/nntrainer/models/model_loader.cpp b/nntrainer/models/model_loader.cpp
index 7f7d9c1..6d438f4 100644 (file)
--- a/nntrainer/models/model_loader.cpp
+++ b/nntrainer/models/model_loader.cpp
@@ -483,7 +483,7 @@ int ModelLoader::loadFromConfig(std::string config, NeuralNetwork &model) {
 
   auto pos = config_realpath.find_last_of("/");
   if (pos == std::string::npos) {
-    ml_loge("resolved model path does not contain any path seperater. %s",
+    ml_loge("resolved model path does not contain any path separator. %s",
             config_realpath.c_str());
     return ML_ERROR_UNKNOWN;
   }

diff --git a/nntrainer/models/model_loader.h b/nntrainer/models/model_loader.h
index 35011f7..1c2032b 100644 (file)
--- a/nntrainer/models/model_loader.h
+++ b/nntrainer/models/model_loader.h
@@ -130,7 +130,7 @@ private:
    * @note  if path is absolute path, return path.
    *        if app_context has working directory set, resolve from app_context
    *        if not, resolve path assuming model_path is the current directory.
-   *        The behavior relys on the semantics of getWorkingPath();
+   *        The behavior relies on the semantics of getWorkingPath();
    * @param path path to resolve
    * @return const std::string resolved path.
    */