[ Application ] Update Logistic Regression to use CCAPI

This patch includes
 . Update Logistic Regression to use CCAPIs
 . Update Inference process
 . static link about openmp

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

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

diff --git a/Applications/LogisticRegression/jni/Android.mk b/Applications/LogisticRegression/jni/Android.mk
index 56e58b6..a049ab6 100644 (file)
--- a/Applications/LogisticRegression/jni/Android.mk
+++ b/Applications/LogisticRegression/jni/Android.mk
@@ -11,16 +11,9 @@ ifndef NNTRAINER_ROOT
 NNTRAINER_ROOT := $(LOCAL_PATH)/../../..
 endif
 
- ML_API_COMMON_INCLUDES := ${NNTRAINER_ROOT}/ml_api_common/include
-NNTRAINER_INCLUDES := $(NNTRAINER_ROOT)/nntrainer \
-       $(NNTRAINER_ROOT)/nntrainer/dataset \
-       $(NNTRAINER_ROOT)/nntrainer/models \
-       $(NNTRAINER_ROOT)/nntrainer/layers \
-       $(NNTRAINER_ROOT)/nntrainer/compiler \
-       $(NNTRAINER_ROOT)/nntrainer/graph \
-       $(NNTRAINER_ROOT)/nntrainer/optimizers \
-       $(NNTRAINER_ROOT)/nntrainer/tensor \
-       $(NNTRAINER_ROOT)/nntrainer/utils \
+ML_API_COMMON_INCLUDES := ${NNTRAINER_ROOT}/ml_api_common/include
+
+NNTRAINER_INCLUDES := $(NNTRAINER_ROOT)/nntrainer/include \
        $(NNTRAINER_ROOT)/api \
        $(NNTRAINER_ROOT)/api/ccapi/include \
        ${ML_API_COMMON_INCLUDES}
@@ -43,12 +36,12 @@ LOCAL_ARM_NEON := true
 LOCAL_CFLAGS += -std=c++17 -Ofast -mcpu=cortex-a53 -Ilz4-nougat/lib
 LOCAL_LDFLAGS += -Llz4-nougat/lib/obj/local/$(TARGET_ARCH_ABI)/
 LOCAL_CXXFLAGS += -std=c++17 -frtti
-LOCAL_CFLAGS += -pthread -fexceptions -fopenmp
+LOCAL_CFLAGS += -pthread -fexceptions -fopenmp -static-openmp
 LOCAL_LDFLAGS += -fexceptions
 LOCAL_MODULE_TAGS := optional
 LOCAL_ARM_MODE := arm
 LOCAL_MODULE := nntrainer_logistic
-LOCAL_LDLIBS := -llog -landroid -fopenmp
+LOCAL_LDLIBS := -llog -landroid -fopenmp -static-openmp
 
 LOCAL_SRC_FILES := main.cpp
 

diff --git a/Applications/LogisticRegression/jni/main.cpp b/Applications/LogisticRegression/jni/main.cpp
index 9c22b1e..d1957d5 100644 (file)
--- a/Applications/LogisticRegression/jni/main.cpp
+++ b/Applications/LogisticRegression/jni/main.cpp
@@ -28,13 +28,12 @@
 #include <cmath>
 #include <fstream>
 #include <iostream>
+#include <layer.h>
+#include <model.h>
+#include <optimizer.h>
 #include <random>
 #include <sstream>
 
-#include <databuffer.h>
-#include <neuralnet.h>
-#include <tensor.h>
-
 std::string data_file;
 
 const unsigned int total_train_data_size = 90;
@@ -52,23 +51,6 @@ constexpr unsigned int SEED = 0;
 bool training = false;
 
 /**
- * @brief     step function
- * @param[in] x value to be distinguished
- * @retval 0.0 or 1.0
- */
-float stepFunction(float x) {
-  if (x > 0.5) {
-    return 1.0;
-  }
-
-  if (x < 0.5) {
-    return 0.0;
-  }
-
-  return x;
-}
-
-/**
  * @brief     get idth Data
  * @param[in] F file stream
  * @param[out] input feature data
@@ -161,64 +143,87 @@ int main(int argc, char *argv[]) {
 
   srand(time(NULL));
 
-  auto data_train = ml::train::createDataset(ml::train::DatasetType::GENERATOR,
-                                             getSample_train);
-
   /**
    * @brief     Create NN
    */
-  std::vector<std::vector<float>> inputVector, outputVector;
-  nntrainer::NeuralNetwork NN;
+  std::unique_ptr<ml::train::Model> model;
+  model = ml::train::createModel(ml::train::ModelType::NEURAL_NET);
 
   /**
    * @brief     Initialize NN with configuration file path
    */
 
   try {
-    NN.loadFromConfig(config);
-    NN.compile();
-    NN.initialize();
+    model->load(config, ml::train::ModelFormat::MODEL_FORMAT_INI);
+    model->compile();
+    model->initialize();
   } catch (...) {
     std::cerr << "Error during init" << std::endl;
     return 0;
   }
 
   if (training) {
-    NN.setDataset(ml::train::DatasetModeType::MODE_TRAIN,
-                  std::move(data_train));
+    auto data_train = ml::train::createDataset(
+      ml::train::DatasetType::GENERATOR, getSample_train);
+
+    model->setDataset(ml::train::DatasetModeType::MODE_TRAIN,
+                      std::move(data_train));
 
     try {
-      NN.train({"save_path=" + weight_path});
+      model->train({"save_path=" + weight_path});
     } catch (...) {
       std::cerr << "Error during train" << std::endl;
       return 0;
     }
   } else {
     try {
-      NN.load(weight_path, ml::train::ModelFormat::MODEL_FORMAT_BIN);
+      model->load(weight_path);
     } catch (std::exception &e) {
       std::cerr << "Error during loading weights: " << e.what() << "\n";
       return 1;
     }
     std::ifstream dataFile(data_file);
     int cn = 0;
+    std::vector<float *> in;
+    std::vector<float *> l;
+
+    auto step = [](const float result) {
+      if (result < 0.5) {
+        return 0;
+      } else
+        return 1;
+    };
+
     for (unsigned int j = 0; j < total_val_data_size; ++j) {
-      nntrainer::Tensor d;
-      std::vector<float> o;
-      std::vector<float> l;
-      o.resize(feature_size);
-      l.resize(1);
 
-      getData(dataFile, o.data(), l.data(), j);
+      float input[feature_size];
+      float label[1];
+
+      if (!getData(dataFile, input, label, j))
+        std::cout << "error dring read file " << std::endl;
 
       try {
-        float answer =
-          NN.forwarding({MAKE_SHARED_TENSOR(nntrainer::Tensor({o}))},
-                        {MAKE_SHARED_TENSOR(nntrainer::Tensor({l}))})[0]
-            ->apply(stepFunction)
-            .getValue(0, 0, 0, 0);
-        std::cout << answer << " : " << l[0] << std::endl;
-        cn += answer == l[0];
+
+        std::vector<float *> answer;
+
+        in.push_back(input);
+        l.push_back(label);
+
+        answer = model->inference(1, in, l);
+
+        in.clear();
+        l.clear();
+
+        int c = step(answer[0][0]);
+
+        if (c == int(label[0])) {
+          cn++;
+          std::cout << answer[0][0] << " - " << c << " : " << label[0]
+                    << std::endl;
+        } else {
+          std::cout << " Something Wrong  " << answer[0][0] << " " << label[0]
+                    << std::endl;
+        }
       } catch (...) {
         std::cerr << "Error during forwarding the model" << std::endl;
         return -1;

diff --git a/Applications/LogisticRegression/res/LogisticRegression.ini b/Applications/LogisticRegression/res/LogisticRegression.ini
index 000d735..a7902c3 100644 (file)
--- a/Applications/LogisticRegression/res/LogisticRegression.ini
+++ b/Applications/LogisticRegression/res/LogisticRegression.ini
@@ -1,7 +1,8 @@
 # Model Section : Model
 [Model]
-Type = Regression      # Network Type : Regression, KNN, NeuralNetwork
-Epochs = 100           # Epochs
+Type = NeuralNetwork   # Network Type : Regression, KNN, NeuralNetwork
+Epochs = 1000          # Epochs
+Learning_rate = 0.001
 Loss = cross           # Loss function : mse (mean squared error)
                   #                 cross ( cross entropy )
 batch_size = 16                # batch size

diff --git a/jni/Android.mk b/jni/Android.mk
index 3de6ccc..10c4beb 100644 (file)
--- a/jni/Android.mk
+++ b/jni/Android.mk
@@ -246,11 +246,11 @@ INIPARSER_SRCS := $(INIPARSER_ROOT)/src/iniparser.c \
 INIPARSER_INCLUDES := $(INIPARSER_ROOT)/src
 
 LOCAL_ARM_NEON      := true
-LOCAL_CFLAGS        += -pthread -fexceptions -fopenmp
+LOCAL_CFLAGS        += -pthread -fexceptions -fopenmp -static-openmp
 LOCAL_CXXFLAGS      += -std=c++17 -frtti -fexceptions
 LOCAL_MODULE_TAGS   := optional
 
-LOCAL_LDLIBS        := -llog -landroid -fopenmp
+LOCAL_LDLIBS        := -llog -landroid -fopenmp -static-openmp
 
 LOCAL_MODULE        := nntrainer
 LOCAL_SRC_FILES     := $(NNTRAINER_SRCS) $(INIPARSER_SRCS)
@@ -320,11 +320,11 @@ CAPI_NNTRAINER_INCLUDES := $(NNTRAINER_ROOT)/nntrainer \
 LOCAL_SHARED_LIBRARIES := ccapi-nntrainer ml-api-inference nntrainer
 
 LOCAL_ARM_NEON      := true
-LOCAL_CFLAGS        += -pthread -fexceptions -fopenmp -DML_API_COMMON=1
+LOCAL_CFLAGS        += -pthread -fexceptions -fopenmp -static-openmp -DML_API_COMMON=1
 LOCAL_CXXFLAGS      += -std=c++17 -frtti -fexceptions -DML_API_COMMON=1
 LOCAL_MODULE_TAGS   := optional
 
-LOCAL_LDLIBS        := -llog -landroid -fopenmp
+LOCAL_LDLIBS        := -llog -landroid -fopenmp -static-openmp
 
 LOCAL_MODULE        := capi-nntrainer
 LOCAL_SRC_FILES     := $(CAPI_NNTRAINER_SRCS)