[property] Set common properties in layer.h

Many layers has common properties which are defined in layer.h
As they are in layer.h and are expected to be used in most layers, lets set their properties in layer.h itself
This reduces a lot of redundancies
If some property is not required to be handled by a specific layer in rare case, handle it as an error in that layer itself

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

diff --git a/api/capi/src/nntrainer.cpp b/api/capi/src/nntrainer.cpp
index 86a811a019257e853f3ef68727d1ff19b0911396..1377af8c5dbf7f553b534f3cd8ca4e1daa85d974 100644 (file)
--- a/api/capi/src/nntrainer.cpp
+++ b/api/capi/src/nntrainer.cpp
@@ -376,7 +376,7 @@ int ml_nnoptimizer_set_property(ml_nnopt_h opt, ...) {
 
   std::shared_ptr<nntrainer::Optimizer> Opt;
   Opt = nnopt->optimizer;
-  
+
   returnable f = [&]() {
     return Opt->setProperty(arg_list);
   };

diff --git a/nntrainer/include/layer.h b/nntrainer/include/layer.h
index 35976f5f35e36bb19892a203036992fa67795487..4089302c0935a58800f1de28b9d58f859d56dfbd 100644 (file)
--- a/nntrainer/include/layer.h
+++ b/nntrainer/include/layer.h
@@ -177,7 +177,7 @@ public:
    * @retval #ML_ERROR_NONE Successful.
    * @retval #ML_ERROR_INVALID_PARAMETER invalid parameter.
    */
-  virtual int setProperty(std::vector<std::string> values) = 0;
+  virtual int setProperty(std::vector<std::string> values);
 
   /**
    * @brief     Optimizer Setter
@@ -338,8 +338,9 @@ public:
    *            11. kernel_size : ( n , m )
    *            12. stride : ( n, m )
    *            13. padding : ( n, m )
-   *            14, pooling_size : ( n,m )
-   *            15, pooling : max, average, global_max, global_average
+   *            14. pooling_size : ( n,m )
+   *            15. pooling : max, average, global_max, global_average
+   *            16. flatten : bool
    */
   enum class PropertyType {
     input_shape = 0,
@@ -364,10 +365,10 @@ public:
 
 protected:
 
-/**
- * @brief        check if current layer's weight decay type is l2norm
- * @return       bool is weightdecay type is L2 Norm
- */
+  /**
+   * @brief     check if current layer's weight decay type is l2norm
+   * @return    bool is weightdecay type is L2 Norm
+   */
   bool isWeightDecayL2Norm() {
     return weight_decay.type == WeightDecayType::l2norm;
   }

diff --git a/nntrainer/src/activation_layer.cpp b/nntrainer/src/activation_layer.cpp
index 24fb48aeb00ee8e658f61bc02b3f2017b0c91bf5..363a3beff8eedae990de59bb1067abb6933604ea 100644 (file)
--- a/nntrainer/src/activation_layer.cpp
+++ b/nntrainer/src/activation_layer.cpp
@@ -135,30 +135,7 @@ void ActivationLayer::setActivation(ActiType acti_type) {
  * @retval #ML_ERROR_INVALID_PARAMETER invalid parameter.
  */
 int ActivationLayer::setProperty(std::vector<std::string> values) {
-  int status = ML_ERROR_NONE;
-
-  if (values.size() != 1) {
-    return ML_ERROR_INVALID_PARAMETER;
-  }
-
-  std::string key;
-  std::string value;
-
-  status = getKeyValue(values[0], key, value);
-  NN_RETURN_STATUS();
-
-  if (static_cast<PropertyType>(parseLayerProperty(key))
-                 != PropertyType::activation) {
-    return ML_ERROR_INVALID_PARAMETER;
-  }
-
-  try {
-    this->setActivation((ActiType)parseType(value, TOKEN_ACTI));
-  } catch (const std::exception &ex) {
-    ml_loge("Error: Not supported Data");
-    return ML_ERROR_INVALID_PARAMETER;
-  }
-  return ML_ERROR_NONE;
+  return Layer::setProperty(values);
 }
 
 }; // namespace nntrainer

diff --git a/nntrainer/src/bn_layer.cpp b/nntrainer/src/bn_layer.cpp
index f660b478d5021282ec70e4f32f9434f1d398c215..afa6b1f4cbad60918a8301d150a1c43802879284 100644 (file)
--- a/nntrainer/src/bn_layer.cpp
+++ b/nntrainer/src/bn_layer.cpp
@@ -65,20 +65,13 @@ int BatchNormalizationLayer::setProperty(std::vector<std::string> values) {
     unsigned int type = parseLayerProperty(key);
 
     switch (static_cast<PropertyType>(type)) {
-    case PropertyType::input_shape:
-      status = dim.setTensorDim(values[0].c_str());
-      break;
-    case PropertyType::bias_init_zero: {
-      status = setBoolean(bias_init_zero, value);
-      NN_RETURN_STATUS();
-    } break;
     case PropertyType::epsilon:
       status = setFloat(epsilon, value);
       NN_RETURN_STATUS();
       break;
     default:
-      ml_loge("Error: Unknown Layer Property Key: %s", key.c_str());
-      status = ML_ERROR_INVALID_PARAMETER;
+      status = Layer::setProperty({values[i]});
+      NN_RETURN_STATUS();
       break;
     }
   }

diff --git a/nntrainer/src/conv2d_layer.cpp b/nntrainer/src/conv2d_layer.cpp
index 5bfdb8ce631d23194816fe524e3b7d6d5fd4c7ce..c45dbf031a67ebc3ff5680762321fa057be2b584 100644 (file)
--- a/nntrainer/src/conv2d_layer.cpp
+++ b/nntrainer/src/conv2d_layer.cpp
@@ -283,36 +283,6 @@ int Conv2DLayer::setProperty(std::vector<std::string> values) {
     unsigned int t = parseLayerProperty(key);
 
     switch (static_cast<PropertyType>(t)) {
-    case PropertyType::input_shape:
-      status = input_dim.setTensorDim(value.c_str());
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::bias_init_zero:
-      status = setBoolean(bias_init_zero, value);
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::activation:
-      status = setActivation((ActiType)parseType(value, TOKEN_ACTI));
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::flatten:
-      status = setBoolean(flatten, value);
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::weight_decay:
-      weight_decay.type = (WeightDecayType)parseType(value, TOKEN_WEIGHT_DECAY);
-      if (weight_decay.type == WeightDecayType::unknown) {
-        ml_loge("Error: Unknown Weight Decay");
-        return ML_ERROR_INVALID_PARAMETER;
-      }
-      break;
-    case PropertyType::weight_decay_lambda:
-      status = setFloat(weight_decay.lambda, value);
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::weight_ini:
-      weight_ini_type = (WeightIniType)parseType(value, TOKEN_WEIGHTINI);
-      break;
     case PropertyType::filter: {
       int size;
       status = setInt(size, value);
@@ -348,8 +318,8 @@ int Conv2DLayer::setProperty(std::vector<std::string> values) {
       NN_RETURN_STATUS();
       break;
     default:
-      ml_loge("Error: Unknown Layer Property Key : %s", key.c_str());
-      status = ML_ERROR_INVALID_PARAMETER;
+      status = Layer::setProperty({values[i]});
+      NN_RETURN_STATUS();
       break;
     }
   }

diff --git a/nntrainer/src/fc_layer.cpp b/nntrainer/src/fc_layer.cpp
index 84b736d0035f031630ada6e8a121897cb1aa079d..7ef6e68e4bc8872ec319c8cda3f881962b380091 100644 (file)
--- a/nntrainer/src/fc_layer.cpp
+++ b/nntrainer/src/fc_layer.cpp
@@ -73,10 +73,6 @@ int FullyConnectedLayer::setProperty(std::vector<std::string> values) {
     unsigned int type = parseLayerProperty(key);
 
     switch (static_cast<PropertyType>(type)) {
-    case PropertyType::input_shape:
-      status = input_dim.setTensorDim(value.c_str());
-      NN_RETURN_STATUS();
-      break;
     case PropertyType::unit: {
       int width;
       status = setInt(width, value);
@@ -84,35 +80,9 @@ int FullyConnectedLayer::setProperty(std::vector<std::string> values) {
       unit = width;
       output_dim.width(unit);
     } break;
-    case PropertyType::bias_init_zero: {
-      status = setBoolean(this->bias_init_zero, value);
-      NN_RETURN_STATUS();
-    } break;
-    case PropertyType::activation:
-      status = setActivation((ActiType)parseType(value, TOKEN_ACTI));
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::flatten:
-      status = setBoolean(flatten, value);
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::weight_decay:
-      weight_decay.type = (WeightDecayType)parseType(value, TOKEN_WEIGHT_DECAY);
-      if (weight_decay.type == WeightDecayType::unknown) {
-        ml_loge("Error: Unknown Weight Decay");
-        return ML_ERROR_INVALID_PARAMETER;
-      }
-      break;
-    case PropertyType::weight_decay_lambda:
-      status = setFloat(weight_decay.lambda, value);
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::weight_ini:
-      weight_ini_type = (WeightIniType)parseType(value, TOKEN_WEIGHTINI);
-      break;
     default:
-      ml_loge("Error: Unknown Layer Property Key : %s", key.c_str());
-      status = ML_ERROR_INVALID_PARAMETER;
+      status = Layer::setProperty({values[i]});
+      NN_RETURN_STATUS();
       break;
     }
   }

diff --git a/nntrainer/src/input_layer.cpp b/nntrainer/src/input_layer.cpp
index c485dc4d697f93441311548f3252d3714a069e6a..5365b4189458c002d60fe8440fbd45c6d281697e 100644 (file)
--- a/nntrainer/src/input_layer.cpp
+++ b/nntrainer/src/input_layer.cpp
@@ -47,14 +47,6 @@ int InputLayer::setProperty(std::vector<std::string> values) {
     unsigned int type = parseLayerProperty(key.c_str());
 
     switch (static_cast<PropertyType>(type)) {
-    case PropertyType::input_shape:
-      status = input_dim.setTensorDim(value.c_str());
-      NN_RETURN_STATUS();
-      break;
-    case PropertyType::bias_init_zero:
-      status = setBoolean(bias_init_zero, value);
-      NN_RETURN_STATUS();
-      break;
     case PropertyType::normalization:
       status = setBoolean(normalization, value);
       NN_RETURN_STATUS();
@@ -64,8 +56,8 @@ int InputLayer::setProperty(std::vector<std::string> values) {
       NN_RETURN_STATUS();
       break;
     default:
-      ml_loge("Error: Unknown Layer Property Key : %s", key.c_str());
-      status = ML_ERROR_INVALID_PARAMETER;
+      status = Layer::setProperty({values[i]});
+      NN_RETURN_STATUS();
       break;
     }
   }

diff --git a/nntrainer/src/layer.cpp b/nntrainer/src/layer.cpp
index b2b50e02778d62e92bad20e10a62b48890f7544b..b6f341f53fd4b934fd979b6b06f8ec388efa12d7 100644 (file)
--- a/nntrainer/src/layer.cpp
+++ b/nntrainer/src/layer.cpp
@@ -118,4 +118,56 @@ std::shared_ptr<std::vector<Tensor>> Layer::getObjFromRef(std::vector<std::refer
   return std::make_shared<std::vector<Tensor>>(std::move(ele));
 }
 
+int Layer::setProperty(std::vector<std::string> values) {
+  int status = ML_ERROR_NONE;
+
+  for (unsigned int i = 0; i < values.size(); ++i) {
+    std::string key;
+    std::string value;
+
+    status = getKeyValue(values[i], key, value);
+    NN_RETURN_STATUS();
+
+    unsigned int type = parseLayerProperty(key);
+
+    switch (static_cast<PropertyType>(type)) {
+    case PropertyType::input_shape:
+      status = input_dim.setTensorDim(value.c_str());
+      NN_RETURN_STATUS();
+      break;
+    case PropertyType::bias_init_zero:
+      status = setBoolean(this->bias_init_zero, value);
+      NN_RETURN_STATUS();
+      break;
+    case PropertyType::activation:
+      status = setActivation((ActiType)parseType(value, TOKEN_ACTI));
+      NN_RETURN_STATUS();
+      break;
+    case PropertyType::flatten:
+      status = setBoolean(flatten, value);
+      NN_RETURN_STATUS();
+      break;
+    case PropertyType::weight_decay:
+      weight_decay.type = (WeightDecayType)parseType(value, TOKEN_WEIGHT_DECAY);
+      if (weight_decay.type == WeightDecayType::unknown) {
+        ml_loge("Error: Unknown Weight Decay");
+        return ML_ERROR_INVALID_PARAMETER;
+      }
+      break;
+    case PropertyType::weight_decay_lambda:
+      status = setFloat(weight_decay.lambda, value);
+      NN_RETURN_STATUS();
+      break;
+    case PropertyType::weight_ini:
+      weight_ini_type = (WeightIniType)parseType(value, TOKEN_WEIGHTINI);
+      break;
+    default:
+      ml_loge("Error: Unknown Layer Property Key : %s", key.c_str());
+      status = ML_ERROR_INVALID_PARAMETER;
+      break;
+    }
+  }
+  return status;
+}
+
 } /* namespace nntrainer */

diff --git a/test/tizen_capi/unittest_tizen_capi.cpp b/test/tizen_capi/unittest_tizen_capi.cpp
index bd684a7fe7943b32acc0f1db7cf8c145a3ba517b..312160c6e88a1505b71f1dac55c6cc46f270a27b 100644 (file)
--- a/test/tizen_capi/unittest_tizen_capi.cpp
+++ b/test/tizen_capi/unittest_tizen_capi.cpp
@@ -266,7 +266,7 @@ TEST(nntrainer_capi_nnmodel, addLayer_02_p) {
 /**
  * @brief Neural Network Model Add Layer Test
  */
-TEST(nntrainer_capi_nnmodel, addLayer_03_n) {
+TEST(nntrainer_capi_nnmodel, addLayer_03_p) {
   int status = ML_ERROR_NONE;
 
   ml_nnmodel_h model;
@@ -280,7 +280,7 @@ TEST(nntrainer_capi_nnmodel, addLayer_03_n) {
 
   status = ml_nnlayer_set_property(layer, "input_shape= 32:1:1:62720",
                                    "activation=sigmoid", NULL);
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+  EXPECT_EQ(status, ML_ERROR_NONE);
 
   status = ml_nnlayer_delete(layer);
   EXPECT_EQ(status, ML_ERROR_NONE);

diff --git a/test/tizen_capi/unittest_tizen_capi_layer.cpp b/test/tizen_capi/unittest_tizen_capi_layer.cpp
index 919291dbca6d28368dcefffa9df61e25455d5b07..cebcc04f617b2f333beb49a6c56c18168161074b 100644 (file)
--- a/test/tizen_capi/unittest_tizen_capi_layer.cpp
+++ b/test/tizen_capi/unittest_tizen_capi_layer.cpp
@@ -97,15 +97,15 @@ TEST(nntrainer_capi_nnlayer, setproperty_02_p) {
 }
 
 /**
- * @brief Neural Network Layer Set Property Test (negative test)
+ * @brief Neural Network Layer Set Property Test (positive test)
  */
-TEST(nntrainer_capi_nnlayer, setproperty_03_n) {
+TEST(nntrainer_capi_nnlayer, setproperty_03_p) {
   ml_nnlayer_h handle;
   int status;
   status = ml_nnlayer_create(&handle, ML_LAYER_TYPE_INPUT);
   EXPECT_EQ(status, ML_ERROR_NONE);
   status = ml_nnlayer_set_property(handle, "activation= sigmoid", NULL);
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+  EXPECT_EQ(status, ML_ERROR_NONE);
   status = ml_nnlayer_delete(handle);
   EXPECT_EQ(status, ML_ERROR_NONE);
 }