From 3c2b8878199851f829dffb53eeed397096bb8564 Mon Sep 17 00:00:00 2001
From: Parichay Kapoor <pk.kapoor@samsung.com>
Date: Tue, 6 Oct 2020 12:34:47 +0900
Subject: [PATCH] [Tensor/model] Move some member functions to private

Move BroadcastInfo to private
Move printMetrics to private

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

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>
---
 nntrainer/include/neuralnet.h | 14 +++++++-------
 nntrainer/include/tensor.h    | 24 ++----------------------
 nntrainer/src/tensor.cpp      | 24 +++++++++++++++++++++++-
 3 files changed, 32 insertions(+), 30 deletions(-)

diff --git a/nntrainer/include/neuralnet.h b/nntrainer/include/neuralnet.h
index 625146d..16b5db7 100644
--- a/nntrainer/include/neuralnet.h
+++ b/nntrainer/include/neuralnet.h
@@ -279,13 +279,6 @@ public:
    */
   void printPreset(std::ostream &out, unsigned int preset);
 
-  /**
-   * @brief print metrics function for neuralnet
-   * @param[in] out outstream
-   * @param[in] flags verbosity from ml_train_summary_type_e
-   */
-  void printMetrics(std::ostream &out, unsigned int flags = 0);
-
 private:
   /**
    * @brief   Print Options when printing layer info
@@ -447,6 +440,13 @@ private:
    * @brief     Update batch size of the model as well as its layers/dataset
    */
   void setBatchSize(unsigned int batch_size);
+
+  /**
+   * @brief print metrics function for neuralnet
+   * @param[in] out outstream
+   * @param[in] flags verbosity from ml_train_summary_type_e
+   */
+  void printMetrics(std::ostream &out, unsigned int flags = 0);
 };
 
 } /* namespace nntrainer */
diff --git a/nntrainer/include/tensor.h b/nntrainer/include/tensor.h
index 260df2d..cb9993c 100644
--- a/nntrainer/include/tensor.h
+++ b/nntrainer/include/tensor.h
@@ -39,28 +39,6 @@ namespace nntrainer {
 class LazyTensor;
 
 /**
- * @struct External Loop Info for broadcasted info
- * @brief External Loop Info for broadcasted iteration. Please refer to
- * DISABLED_private_external_loop_n in unittest_nntrainer_tensor.
- * @note This should better be implemented in iterator fashion before used
- * extensively.
- */
-struct BroadcastInfo {
-
-  /**
-   * @brief Construct a new External Loop Info object
-   *
-   */
-  BroadcastInfo() : strides{0, 0, 0, 0} {}
-
-  unsigned int buffer_size; /**< virtual size of the buffer */
-  int buffer_axis;          /**< the smallest axis that should be looped.
-                                 -1 means no loop needed*/
-  std::array<unsigned int, MAXDIM>
-    strides; /**< modified strides for the loop */
-};
-
-/**
  * @class   Tensor Class for Calculation
  * @brief   Tensor Class for Calculation
  */
@@ -593,6 +571,8 @@ private:
     return (b * strides[0] + c * strides[1] + h * strides[2] + w * strides[3]);
   }
 
+  struct BroadcastInfo;
+
   /**
    * @brief Applies the given operator to the tensor with the passed argument
    * @param[in] m Tensor
diff --git a/nntrainer/src/tensor.cpp b/nntrainer/src/tensor.cpp
index 16c1378..ff0de42 100644
--- a/nntrainer/src/tensor.cpp
+++ b/nntrainer/src/tensor.cpp
@@ -208,6 +208,28 @@ int Tensor::add_i(float const &value) {
 Tensor Tensor::add(float const &value) { CLONE_OP_I(add_i, value); }
 
 /**
+ * @struct External Loop Info for broadcasted info
+ * @brief External Loop Info for broadcasted iteration. Please refer to
+ * DISABLED_private_external_loop_n in unittest_nntrainer_tensor.
+ * @note This should better be implemented in iterator fashion before used
+ * extensively.
+ */
+struct Tensor::BroadcastInfo {
+
+  /**
+   * @brief Construct a new External Loop Info object
+   *
+   */
+  BroadcastInfo() : strides{0, 0, 0, 0} {}
+
+  unsigned int buffer_size; /**< virtual size of the buffer */
+  int buffer_axis;          /**< the smallest axis that should be looped.
+                                 -1 means no loop needed*/
+  std::array<unsigned int, MAXDIM>
+    strides; /**< modified strides for the loop */
+};
+
+/**
  * @brief Add Tensor Element by Element without mem copy
  * @param[in] m Tensor to be added
  * #retval #ML_ERROR_NONE  Successful
@@ -798,7 +820,7 @@ Tensor Tensor::standardization() const {
   return result;
 }
 
-BroadcastInfo Tensor::computeBroadcastInfo(const Tensor &m) {
+Tensor::BroadcastInfo Tensor::computeBroadcastInfo(const Tensor &m) {
   if (m.length() > this->length())
     throw exception::not_supported("broadcasting *this is not supported");
 
-- 
2.7.4