From 10be43e689c0e9bcd2a6fe4b45c8e6777f0021fa Mon Sep 17 00:00:00 2001
From: Jihoon Lee <jhoon.it.lee@samsung.com>
Date: Thu, 7 Oct 2021 01:38:50 +0900
Subject: [PATCH] [Neuralnet] Add property of input layers, label layers

This patch add property, input layers and label layers to neuralnet

**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>
---
 nntrainer/layers/common_properties.cpp | 13 +++++++++----
 nntrainer/layers/common_properties.h   | 26 ++++++++++++++++++++++++++
 nntrainer/layers/layer_node.cpp        | 13 -------------
 nntrainer/models/neuralnet.cpp         |  2 +-
 nntrainer/models/neuralnet.h           |  5 ++++-
 5 files changed, 40 insertions(+), 19 deletions(-)

diff --git a/nntrainer/layers/common_properties.cpp b/nntrainer/layers/common_properties.cpp
index 7a7270a..eaf8140 100644
--- a/nntrainer/layers/common_properties.cpp
+++ b/nntrainer/layers/common_properties.cpp
@@ -76,8 +76,7 @@ bool NumClass::isValid(const unsigned int &v) const { return v > 0; }
 
 ConnectionSpec::ConnectionSpec(const std::vector<props::Name> &layer_ids_,
                                const std::string &op_type_) :
-  op_type(op_type_),
-  layer_ids(layer_ids_) {
+  op_type(op_type_), layer_ids(layer_ids_) {
   NNTR_THROW_IF((op_type != ConnectionSpec::NoneType && layer_ids.size() < 2),
                 std::invalid_argument)
     << "connection type is not none but has only a single or empty layer id, "
@@ -94,8 +93,8 @@ ConnectionSpec::ConnectionSpec(const std::vector<props::Name> &layer_ids_,
 ConnectionSpec::ConnectionSpec(const ConnectionSpec &rhs) = default;
 ConnectionSpec &ConnectionSpec::operator=(const ConnectionSpec &rhs) = default;
 ConnectionSpec::ConnectionSpec(ConnectionSpec &&rhs) noexcept = default;
-ConnectionSpec &ConnectionSpec::
-operator=(ConnectionSpec &&rhs) noexcept = default;
+ConnectionSpec &
+ConnectionSpec::operator=(ConnectionSpec &&rhs) noexcept = default;
 
 bool ConnectionSpec::operator==(const ConnectionSpec &rhs) const {
   return op_type == rhs.op_type && layer_ids == rhs.layer_ids;
@@ -222,6 +221,12 @@ bool WeightRegularizerConstant::isValid(const float &value) const {
   return value >= 0.0f;
 }
 
+InputLayer::InputLayer() : Name() {}
+InputLayer::InputLayer(const std::string &name) : Name(name) {}
+
+LabelLayer::LabelLayer() : Name() {}
+LabelLayer::LabelLayer(const std::string &name) : Name(name) {}
+
 HiddenStateActivation::HiddenStateActivation(ActivationTypeInfo::Enum value) {
   set(value);
 };
diff --git a/nntrainer/layers/common_properties.h b/nntrainer/layers/common_properties.h
index c038f63..b313908 100644
--- a/nntrainer/layers/common_properties.h
+++ b/nntrainer/layers/common_properties.h
@@ -581,6 +581,32 @@ public:
   bool isValid(const float &value) const override;
 };
 
+/**
+ * @brief Input Layer name property which saves a single connection
+ * (practically, std::vector<InputLayers> is used)
+ *
+ */
+class InputLayer : public Name {
+public:
+  InputLayer();
+  InputLayer(const std::string &name);
+  static constexpr const char *key = "input_layers";
+  using prop_tag = str_prop_tag;
+};
+
+/**
+ * @brief label Layer name property which saves a single
+ * connection (practically, std::vector<LabelLayers> is used)
+ *
+ */
+class LabelLayer : public Name {
+public:
+  LabelLayer();
+  LabelLayer(const std::string &name);
+  static constexpr const char *key = "label_layers";
+  using prop_tag = str_prop_tag;
+};
+
 /******** below section is for enumerations ***************/
 /**
  * @brief     Enumeration of activation function type
diff --git a/nntrainer/layers/layer_node.cpp b/nntrainer/layers/layer_node.cpp
index d862679..60013a1 100644
--- a/nntrainer/layers/layer_node.cpp
+++ b/nntrainer/layers/layer_node.cpp
@@ -54,19 +54,6 @@ public:
 };
 
 /**
- * @brief Input Layer name property which saves a single connection
- * (practically, std::vector<InputLayers> is used)
- *
- */
-class InputLayer : public Name {
-public:
-  InputLayer() : Name(){};
-  InputLayer(const std::string &name) : Name(name) {}
-  static constexpr const char *key = "input_layers";
-  using prop_tag = str_prop_tag;
-};
-
-/**
  * @brief Loss property, this defines loss specification of layer
  *
  */
diff --git a/nntrainer/models/neuralnet.cpp b/nntrainer/models/neuralnet.cpp
index 9810c16..33610e2 100644
--- a/nntrainer/models/neuralnet.cpp
+++ b/nntrainer/models/neuralnet.cpp
@@ -48,7 +48,7 @@
 namespace nntrainer {
 
 NeuralNetwork::NeuralNetwork(AppContext app_context_, bool in_place_opt) :
-  model_props(props::LossType()),
+  model_props(props::LossType(), {}, {}),
   model_flex_props(props::Epochs(), props::TrainingBatchSize(),
                    props::SavePath(), props::ContinueTrain(),
                    props::SaveBestPath()),
diff --git a/nntrainer/models/neuralnet.h b/nntrainer/models/neuralnet.h
index 86fcb00..5555cdb 100644
--- a/nntrainer/models/neuralnet.h
+++ b/nntrainer/models/neuralnet.h
@@ -34,6 +34,7 @@
 #endif
 
 #include <app_context.h>
+#include <common_properties.h>
 #include <dynamic_training_optimization.h>
 #include <execution_mode.h>
 #include <layer_node.h>
@@ -477,7 +478,9 @@ private:
   using FlexiblePropTypes =
     std::tuple<props::Epochs, props::TrainingBatchSize, props::SavePath,
                props::ContinueTrain, props::SaveBestPath>;
-  using RigidPropTypes = std::tuple<props::LossType>;
+  using RigidPropTypes =
+    std::tuple<props::LossType, std::vector<props::InputLayer>,
+               std::vector<props::LabelLayer>>;
 
   RigidPropTypes model_props;         /**< model props */
   FlexiblePropTypes model_flex_props; /**< model train props */
-- 
2.7.4