Separate databuffer_file unittest

 Separate databuffer_file unittest for further development (#61)

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

diff --git a/test/unittest/meson.build b/test/unittest/meson.build
index 69f0843..55eae43 100644 (file)
--- a/test/unittest/meson.build
+++ b/test/unittest/meson.build
@@ -37,3 +37,12 @@ unittest_util_func = executable('unittest_util_func',
   install_dir: application_install_dir
 )
 test('unittest_util_func', unittest_util_func)
+
+unittest_databuffer_file = executable('unittest_databuffer_file',
+  ['../nntrainer_test_util.cpp', 'unittest_databuffer_file.cpp'],
+  dependencies: [unittest_nntrainer_internal_deps],
+  include_directories: nntrainer_test_inc,
+  install: get_option('enable-test'),
+  install_dir: application_install_dir
+)
+test('unittest_databuffer_file', unittest_databuffer_file)

diff --git a/test/unittest/unittest_databuffer_file.cpp b/test/unittest/unittest_databuffer_file.cpp
new file mode 100644 (file)
index 0000000..170d8fc
--- /dev/null
+++ b/test/unittest/unittest_databuffer_file.cpp
@@ -0,0 +1,208 @@
+/**
+ * Copyright (C) 2020 Samsung Electronics Co., Ltd. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file        unittest_nntrainer_databuffer_file.cpp
+ * @date        10 April 2020
+ * @brief       Unit test databuffer from file.
+ * @see         https://github.com/nnstreamer/nntrainer
+ * @author      Jijoong Moon <jijoong.moon@samsung.com>
+ * @bug         No known bugs
+ */
+#include "nntrainer_test_util.h"
+#include <databuffer_file.h>
+#include <fstream>
+#include <nntrainer_error.h>
+
+/**
+ * @brief Data Buffer
+ */
+TEST(nntrainer_DataBuffer, setFeatureSize_01_p) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setClassNum(10);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("./trainingSet.dat", nntrainer::DATA_TRAIN);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setFeatureSize(62720);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+}
+
+/**
+ * @brief Data Buffer
+ */
+TEST(nntrainer_DataBuffer, setFeatureSize_02_n) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setClassNum(10);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("./trainingSet.dat", nntrainer::DATA_TRAIN);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setFeatureSize(0);
+  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
+
+/**
+ * @brief Data Buffer
+ */
+TEST(nntrainer_DataBuffer, setMiniBatch_01_p) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setMiniBatch(32);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+}
+
+/**
+ * @brief Data Buffer
+ */
+TEST(nntrainer_DataBuffer, setMiniBatch_02_n) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setMiniBatch(0);
+  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
+
+/**
+ * @brief Data Buffer
+ */
+TEST(nntrainer_DataBuffer, init_01_p) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setMiniBatch(32);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setClassNum(10);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("trainingSet.dat", nntrainer::DATA_TRAIN);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("valSet.dat", nntrainer::DATA_VAL);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("testSet.dat", nntrainer::DATA_TEST);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("label.dat", nntrainer::DATA_LABEL);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setFeatureSize(62720);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.init();
+  EXPECT_EQ(status, ML_ERROR_NONE);
+}
+
+/**
+ * @brief Data Buffer
+ */
+TEST(nntrainer_DataBuffer, init_02_n) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setMiniBatch(32);
+  status = data_buffer.setClassNum(10);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("trainingSet.dat", nntrainer::DATA_TRAIN);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("valSet.dat", nntrainer::DATA_VAL);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("testSet.dat", nntrainer::DATA_TEST);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("label.dat", nntrainer::DATA_LABEL);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.init();
+  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
+
+/**
+ * @brief Data Buffer set number of Class
+ */
+TEST(nntrainer_DataBuffer, setClassNum_01_p) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setClassNum(3);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setClassNum(0);
+  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
+
+/**
+ * @brief Data Buffer set number of Class
+ */
+TEST(nntrainer_DataBuffer, setClassNum_02_n) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setClassNum(0);
+  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
+
+/**
+ * @brief Data Buffer set train Data file
+ */
+TEST(nntrainer_DataBuffer, setDataFile_01_p) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setDataFile("./trainingSet.dat", nntrainer::DATA_TRAIN);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+}
+
+/**
+ * @brief Data Buffer set train Data file
+ */
+TEST(nntrainer_DataBuffer, setDataFile_02_n) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setDataFile("./no_exist.dat", nntrainer::DATA_TRAIN);
+  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
+
+/**
+ * @brief Data Buffer set train Data file
+ */
+TEST(nntrainer_DataBuffer, setDataFile_03_p) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setClassNum(10);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("./label.dat", nntrainer::DATA_LABEL);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+}
+
+/**
+ * @brief Data Buffer set train Data file
+ */
+TEST(nntrainer_DataBuffer, setDataFile_04_n) {
+  int status = ML_ERROR_NONE;
+  nntrainer::DataBufferFromDataFile data_buffer;
+  status = data_buffer.setClassNum(3);
+  EXPECT_EQ(status, ML_ERROR_NONE);
+  status = data_buffer.setDataFile("./label.dat", nntrainer::DATA_LABEL);
+  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
+
+
+/**
+ * @brief Main gtest
+ */
+int main(int argc, char **argv) {
+  int result = -1;
+
+  try {
+    testing::InitGoogleTest (&argc, argv);
+  } catch (...) {
+    ml_loge("Failed to init gtest\n");
+  }
+
+  try {
+    result = RUN_ALL_TESTS();
+  } catch (...) {
+    ml_loge("Failed to run test.\n");
+  }
+
+  return result;
+}
+

diff --git a/test/unittest/unittest_nntrainer_internal.cpp b/test/unittest/unittest_nntrainer_internal.cpp
index 676336b..06a77e6 100644 (file)
--- a/test/unittest/unittest_nntrainer_internal.cpp
+++ b/test/unittest/unittest_nntrainer_internal.cpp
@@ -13,7 +13,7 @@
  */
 
 /**
- * @file        unittest_nntrainer_interanl.cpp
+ * @file        unittest_nntrainer_internal.cpp
  * @date        10 April 2020
  * @brief       Unit test utility.
  * @see         https://github.com/nnstreamer/nntrainer
@@ -212,72 +212,6 @@ TEST(nntrainer_NeuralNetwork, init_011_p) {
 }
 
 /**
- * @brief Data Buffer set number of Class
- */
-TEST(nntrainer_DataBuffer, setClassNum_01_p) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setClassNum(3);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setClassNum(0);
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-}
-
-/**
- * @brief Data Buffer set number of Class
- */
-TEST(nntrainer_DataBuffer, setClassNum_02_n) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setClassNum(0);
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-}
-
-/**
- * @brief Data Buffer set train Data file
- */
-TEST(nntrainer_DataBuffer, setDataFile_01_p) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setDataFile("./trainingSet.dat", nntrainer::DATA_TRAIN);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-}
-
-/**
- * @brief Data Buffer set train Data file
- */
-TEST(nntrainer_DataBuffer, setDataFile_02_n) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setDataFile("./no_exist.dat", nntrainer::DATA_TRAIN);
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-}
-
-/**
- * @brief Data Buffer set train Data file
- */
-TEST(nntrainer_DataBuffer, setDataFile_03_p) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setClassNum(10);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("./label.dat", nntrainer::DATA_LABEL);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-}
-
-/**
- * @brief Data Buffer set train Data file
- */
-TEST(nntrainer_DataBuffer, setDataFile_04_n) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setClassNum(3);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("./label.dat", nntrainer::DATA_LABEL);
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-}
-
-/**
  * @brief Optimizer set type
  */
 TEST(nntrainer_Optimizer, setType_01_p) {
@@ -617,99 +551,6 @@ TEST(nntrainer_BatchNormalizationLayer, setActivation_02_n) {
   EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
 }
 
-/**
- * @brief Data Buffer
- */
-TEST(nntrainer_DataBuffer, setFeatureSize_01_p) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setClassNum(10);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("./trainingSet.dat", nntrainer::DATA_TRAIN);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setFeatureSize(62720);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-}
-
-/**
- * @brief Data Buffer
- */
-TEST(nntrainer_DataBuffer, setFeatureSize_02_n) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setClassNum(10);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("./trainingSet.dat", nntrainer::DATA_TRAIN);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setFeatureSize(0);
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-}
-
-/**
- * @brief Data Buffer
- */
-TEST(nntrainer_DataBuffer, setMiniBatch_01_p) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setMiniBatch(32);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-}
-
-/**
- * @brief Data Buffer
- */
-TEST(nntrainer_DataBuffer, setMiniBatch_02_n) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setMiniBatch(0);
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-}
-
-/**
- * @brief Data Buffer
- */
-TEST(nntrainer_DataBuffer, init_01_p) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setMiniBatch(32);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setClassNum(10);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("trainingSet.dat", nntrainer::DATA_TRAIN);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("valSet.dat", nntrainer::DATA_VAL);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("testSet.dat", nntrainer::DATA_TEST);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("label.dat", nntrainer::DATA_LABEL);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setFeatureSize(62720);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.init();
-  EXPECT_EQ(status, ML_ERROR_NONE);
-}
-
-/**
- * @brief Data Buffer
- */
-TEST(nntrainer_DataBuffer, init_02_n) {
-  int status = ML_ERROR_NONE;
-  nntrainer::DataBufferFromDataFile data_buffer;
-  status = data_buffer.setMiniBatch(32);
-  status = data_buffer.setClassNum(10);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("trainingSet.dat", nntrainer::DATA_TRAIN);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("valSet.dat", nntrainer::DATA_VAL);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("testSet.dat", nntrainer::DATA_TEST);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.setDataFile("label.dat", nntrainer::DATA_LABEL);
-  EXPECT_EQ(status, ML_ERROR_NONE);
-  status = data_buffer.init();
-  EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-}
-
 TEST(nntrainer_TensorDim, setTensorDim_01_p) {
   int status = ML_ERROR_NONE;