[application] add onnx example

added basic onnx application example.

It loads `add_example.onnx` file and create nntrainer network graph.
(network structure is "input + bias = output")

Output of this example is as below:

================================================================================
          Layer name          Layer type    Output dimension         Input layer
================================================================================
               input               input             1:1:1:2
--------------------------------------------------------------------------------
                bias              weight             1:1:1:2
--------------------------------------------------------------------------------
                 add                 add             1:1:1:2               input
                                                                            bias
================================================================================

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

Signed-off-by: Seungbaek Hong <[email protected]>

Applications/ONNX/jni/Android.mk

@@ -0,0 +1,60 @@
+LOCAL_PATH := $(call my-dir)
+
+include $(CLEAR_VARS)
+
+# ndk path
+ifndef ANDROID_NDK
+$(error ANDROID_NDK is not defined!)
+endif
+
+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 \
+	$(NNTRAINER_ROOT)/nntrainer/converter \
+	$(NNTRAINER_ROOT)/api \
+	$(NNTRAINER_ROOT)/api/ccapi/include \
+	${ML_API_COMMON_INCLUDES}
+
+LOCAL_MODULE := nntrainer
+LOCAL_SRC_FILES := $(NNTRAINER_ROOT)/libs/$(TARGET_ARCH_ABI)/libnntrainer.so
+
+include $(PREBUILT_SHARED_LIBRARY)
+
+include $(CLEAR_VARS)
+
+LOCAL_MODULE := ccapi-nntrainer
+LOCAL_SRC_FILES := $(NNTRAINER_ROOT)/libs/$(TARGET_ARCH_ABI)/libccapi-nntrainer.so
+
+include $(PREBUILT_SHARED_LIBRARY)
+
+include $(CLEAR_VARS)
+
+LOCAL_ARM_NEON := true
+LOCAL_CFLAGS += -std=c++17 -Ofast -mcpu=cortex-a53 -Ilz4-nougat/lib
+LOCAL_LDFLAGS += -Llz4-nougat/lib/obj/local/$(TARGET_ARCH_ABI)/
+LOCAL_CXXFLAGS += -std=c++17 -frtti
+LOCAL_CFLAGS += -pthread -fexceptions -fopenmp
+LOCAL_LDFLAGS += -fexceptions
+LOCAL_MODULE_TAGS := optional
+LOCAL_ARM_MODE := arm
+LOCAL_MODULE := nntrainer_onnx_example
+LOCAL_LDLIBS := -llog -landroid -fopenmp
+
+LOCAL_SRC_FILES := main.cpp
+
+LOCAL_SHARED_LIBRARIES := nntrainer ccapi-nntrainer
+
+LOCAL_C_INCLUDES += $(NNTRAINER_INCLUDES)
+
+include $(BUILD_EXECUTABLE)

Applications/ONNX/jni/Application.mk

@@ -0,0 +1,4 @@
+APP_ABI := arm64-v8a
+APP_STL := c++_shared
+APP_PLATFORM := android-29
+APP_SUPPORT_FLEXIBLE_PAGE_SIZES := true

Applications/ONNX/jni/add_example.onnx

@@ -0,0 +1,14 @@
+pytorch2.3.1:{
+
+input
+biasoutput/Add"Add
+main_graph*


BbiasJ�B�?ɉ.�Z
+input
+
+

+b
+output
+

+

+

+B

Applications/ONNX/jni/main.cpp

@@ -0,0 +1,47 @@
+// SPDX-License-Identifier: Apache-2.0
+/**
+ * Copyright (C) 2025 Seungbaek Hong <[email protected]>
+ *
+ * @file   main.cpp
+ * @date   26 Feb 2025
+ * @brief  onnx example using nntrainer-onnx-api
+ * @see    https://github.com/nnstreamer/nntrainer
+ * @author Seungbaek Hong <[email protected]>
+ * @bug    No known bugs except for NYI items
+ */
+
+#include <iostream>
+#include <layer.h>
+#include <model.h>
+#include <nntrainer-api-common.h>
+#include <onnx.h>
+#include <optimizer.h>
+#include <sstream>
+#include <util_func.h>
+
+using ModelHandle = std::unique_ptr<ml::train::Model>;
+
+int main() {
+  ModelHandle model = ml::train::loadONNX("../../../../Applications/ONNX/"
+                                          "jni/add_example.onnx");
+
+  model->setProperty({nntrainer::withKey("batch_size", 1)});
+
+  try {
+    model->compile();
+  } catch (const std::exception &e) {
+    std::cerr << "Error during compile: " << e.what() << "\n";
+    return 1;
+  }
+
+  try {
+    model->initialize();
+  } catch (const std::exception &e) {
+    std::cerr << "Error during initialize: " << e.what() << "\n";
+    return 1;
+  }
+
+  model->summarize(std::cout, ML_TRAIN_SUMMARY_MODEL);
+
+  return 0;
+}

Applications/ONNX/jni/meson.build

@@ -0,0 +1,21 @@
+onnx_example_sources = [
+  'main.cpp',
+]
+
+onnx_example_dependencies = [app_utils_dep,
+  iniparser_dep,
+  nntrainer_dep,
+  nntrainer_ccapi_dep
+]
+
+if get_option('enable-test')
+  onnx_example_dependencies += [gtest_dep]
+endif
+
+e = executable('nntrainer_onnx_example',
+  onnx_example_sources,
+  include_directories: [include_directories('.')],
+  dependencies: onnx_example_dependencies,
+  install: get_option('install-app'),
+  install_dir: application_install_dir
+)

Applications/meson.build

@@ -29,5 +29,5 @@ if get_option('enable-tflite-backbone')
   subdir('SimpleShot')
 endif
 subdir('PicoGPT/jni')
-
+subdir('ONNX/jni')
 subdir('SimpleFC/jni')

nntrainer/schema/onnx_interpreter.h

@@ -16,51 +16,14 @@
 
 #include <app_context.h>
 #include <interpreter.h>
+#include <iostream>
 #include <layer.h>
 #include <layer_node.h>
 #include <model.h>
 #include <nntrainer-api-common.h>
 #include <onnx.pb.h>
 #include <string>
-
-/**
- * @brief make "key=value" from key and value
- *
- * @tparam T type of a value
- * @param key key
- * @param value value
- * @return std::string with "key=value"
- */
-template <typename T>
-static std::string withKey(const std::string &key, const T &value) {
-  std::stringstream ss;
-  ss << key << "=" << value;
-  return ss.str();
-}
-
-/**
- * @brief make "key=value1,value2,...valueN" from key and multiple values
- *
- * @tparam T type of a value
- * @param key key
- * @param value list of values
- * @return std::string with "key=value1,value2,...valueN"
- */
-template <typename T>
-static std::string withKey(const std::string &key,
-                           std::initializer_list<T> value) {
-  if (std::empty(value)) {
-    throw std::invalid_argument("empty data cannot be converted");
-  }
-  std::stringstream ss;
-  ss << key << "=";
-  auto iter = value.begin();
-  for (; iter != value.end() - 1; ++iter) {
-    ss << *iter << ',';
-  }
-  ss << *iter;
-  return ss.str();
-}
+#include <util_func.h>
 
 namespace nntrainer {
 /**
@@ -103,8 +66,9 @@ class ONNXInterpreter {
 
       // weight layer should be modified not to use input_shape as a parameter
       layers.push_back(ml::train::createLayer(
-        "weight", {withKey("name", cleanName(initializer.name())),
-                   withKey("dim", dim), withKey("input_shape", dim)}));
+        "weight", {nntrainer::withKey("name", cleanName(initializer.name())),
+                   nntrainer::withKey("dim", dim),
+                   nntrainer::withKey("input_shape", dim)}));
     }
 
     // Create input & constant tensor layer
@@ -120,8 +84,8 @@ class ONNXInterpreter {
       if (input.name().find("input") !=
           std::string::npos) { // Create input layer
         layers.push_back(ml::train::createLayer(
-          "input", {withKey("name", cleanName(input.name())),
-                    withKey("input_shape", dim)}));
+          "input", {nntrainer::withKey("name", cleanName(input.name())),
+                    nntrainer::withKey("input_shape", dim)}));
       } else { // Create constant tensor layer
         throw std::runtime_error("Constant tensors are not supported yet.");
       }
@@ -152,9 +116,9 @@ class ONNXInterpreter {
           {cleanName(node.output()[0]), cleanName(node.name())});
 
         layers.push_back(ml::train::createLayer(
-          "add",
-          {"name=" + cleanName(node.name()),
-           withKey("input_layers", inputNames[0] + "," + inputNames[1])}));
+          "add", {"name=" + cleanName(node.name()),
+                  nntrainer::withKey("input_layers",
+                                     inputNames[0] + "," + inputNames[1])}));
       } else {
         throw std::runtime_error("Unsupported operation type: " +
                                  node.op_type());