[test] Bug fix for unittest

ccapi and capi use the same filenames for their ini files which can be
buggy if the unittests are run in parallel or unittests differ.
Make ini untitest names independent.

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

diff --git a/test/ccapi/unittest_ccapi.cpp b/test/ccapi/unittest_ccapi.cpp
index c84693cfc5e729ac5d71bf417b353551313c8126..7a25a7dd49572903f8b320ac1f80357d8f6e4b79 100644 (file)
--- a/test/ccapi/unittest_ccapi.cpp
+++ b/test/ccapi/unittest_ccapi.cpp
@@ -196,7 +196,7 @@ static nntrainer::IniSection outputlayer("outputlayer",
  */
 TEST(nntrainer_ccapi, train_with_config_01_p) {
   std::unique_ptr<ml::train::Model> model;
-  ScopedIni s("test_train_01_p",
+  ScopedIni s("ccapi_test_train_01_p",
               {model_base + "batch_size = 16", optimizer, learning_rate,
                dataset + "-BufferSize", inputlayer, outputlayer});
 
@@ -394,7 +394,7 @@ TEST(nntrainer_ccapi, train_batch_size_update_after) {
  */
 TEST(nntrainer_ccapi, train_with_config_02_n) {
   std::unique_ptr<ml::train::Model> model;
-  ScopedIni s("test_train_01_p",
+  ScopedIni s("ccapi_test_train_01_p",
               {model_base + "batch_size = 16", dataset + "-BufferSize",
                inputlayer, outputlayer});
 
@@ -410,7 +410,7 @@ TEST(nntrainer_ccapi, train_with_config_02_n) {
 TEST(nntrainer_ccapi, save_ini_p) {
   std::unique_ptr<ml::train::Model> model;
   model = ml::train::createModel(ml::train::ModelType::NEURAL_NET);
-  ScopedIni s("simple_ini",
+  ScopedIni s("ccapi_simple_ini",
               {model_base + "batch_size = 16", optimizer, learning_rate,
                dataset + "-BufferSize", inputlayer, outputlayer});
 

diff --git a/test/tizen_capi/unittest_tizen_capi.cpp b/test/tizen_capi/unittest_tizen_capi.cpp
index e438c5f3068fe322df5fd8ff5d0c5a653cb5ba4f..b617650b981ed911f23f61646d2f7614a0d65e15 100644 (file)
--- a/test/tizen_capi/unittest_tizen_capi.cpp
+++ b/test/tizen_capi/unittest_tizen_capi.cpp
@@ -137,7 +137,7 @@ TEST(nntrainer_capi_nnmodel, compile_01_p) {
   ml_train_model_h handle = NULL;
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_compile_01_p",
+  ScopedIni s("capi_test_compile_01_p",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
 
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle);
@@ -166,7 +166,7 @@ TEST(nntrainer_capi_nnmodel, construct_conf_02_n) {
   ml_train_model_h handle = NULL;
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_compile_03_n",
+  ScopedIni s("capi_test_compile_03_n",
               {model_base, optimizer, dataset, inputlayer + "Input_Shape=1:1:0",
                outputlayer});
 
@@ -334,7 +334,7 @@ TEST(nntrainer_capi_nnmodel, train_01_p) {
   ml_train_model_h handle = NULL;
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_train_01_p",
+  ScopedIni s("capi_test_train_01_p",
               {model_base + "batch_size = 16", optimizer,
                dataset + "-BufferSize", inputlayer, outputlayer});
 
@@ -369,7 +369,7 @@ TEST(nntrainer_capi_nnmodel, train_02_n) {
 TEST(nntrainer_capi_nnmodel, train_03_n) {
   ml_train_model_h handle = NULL;
   int status = ML_ERROR_NONE;
-  ScopedIni s("test_train_01_p",
+  ScopedIni s("capi_test_train_01_p",
               {model_base + "batch_size = 16", optimizer,
                dataset + "-BufferSize", inputlayer, outputlayer});
 
@@ -518,7 +518,7 @@ TEST(nntrainer_capi_nnmodel, addLayer_05_n) {
   ml_train_model_h model = NULL;
   ml_train_layer_h layer = NULL;
 
-  ScopedIni s("test_compile_01_p",
+  ScopedIni s("capi_test_compile_01_p",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
 
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &model);
@@ -1057,7 +1057,7 @@ TEST(nntrainer_capi_summary, summary_01_p) {
   ml_train_model_h handle = NULL;
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_compile_01_p",
+  ScopedIni s("capi_test_compile_01_p",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle);
   EXPECT_EQ(status, ML_ERROR_NONE);
@@ -1083,7 +1083,7 @@ TEST(nntrainer_capi_summary, summary_02_n) {
   ml_train_model_h handle = NULL;
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_compile_01_p",
+  ScopedIni s("capi_test_compile_01_p",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle);
   EXPECT_EQ(status, ML_ERROR_NONE);
@@ -1109,7 +1109,7 @@ TEST(nntrainer_capi_nnmodel, get_input_output_dimension_01_p) {
 
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_get_input_dimension_01_p",
+  ScopedIni s("capi_test_get_input_dimension_01_p",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle);
   EXPECT_EQ(status, ML_ERROR_NONE);
@@ -1163,7 +1163,7 @@ TEST(nntrainer_capi_nnmodel, get_input_output_dimension_02_p) {
 
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_get_input_dimension_02_p",
+  ScopedIni s("capi_test_get_input_dimension_02_p",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle);
   EXPECT_EQ(status, ML_ERROR_NONE);
@@ -1211,7 +1211,7 @@ TEST(nntrainer_capi_nnmodel, get_input_output_dimension_03_n) {
 
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_get_input_dimension_03_n",
+  ScopedIni s("capi_test_get_input_dimension_03_n",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle);
   EXPECT_EQ(status, ML_ERROR_NONE);
@@ -1241,7 +1241,7 @@ TEST(nntrainer_capi_nnmodel, get_input_output_dimension_05_n) {
 
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_get_input_dimension_05_n",
+  ScopedIni s("capi_test_get_input_dimension_05_n",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle);
   EXPECT_EQ(status, ML_ERROR_NONE);
@@ -1270,7 +1270,7 @@ TEST(nntrainer_capi_nnmodel, get_input_output_dimension_06_n) {
 
   int status = ML_ERROR_NONE;
 
-  ScopedIni s("test_get_input_dimension_06_n",
+  ScopedIni s("capi_test_get_input_dimension_06_n",
               {model_base, optimizer, dataset, inputlayer, outputlayer});
   status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle);
   EXPECT_EQ(status, ML_ERROR_NONE);