From cc4728e5c939d7b3622652af726edfad87ac0358 Mon Sep 17 00:00:00 2001 From: hyeonseok lee Date: Thu, 13 Apr 2023 16:34:57 +0900 Subject: [PATCH] [test] reorder tizen capi unittest - Reorder unittest for sequential order Signed-off-by: hyeonseok lee --- test/tizen_capi/unittest_tizen_capi.cpp | 254 +++++++++++----------- test/tizen_capi/unittest_tizen_capi_optimizer.cpp | 16 +- 2 files changed, 135 insertions(+), 135 deletions(-) diff --git a/test/tizen_capi/unittest_tizen_capi.cpp b/test/tizen_capi/unittest_tizen_capi.cpp index eed6674..24c2aeb 100644 --- a/test/tizen_capi/unittest_tizen_capi.cpp +++ b/test/tizen_capi/unittest_tizen_capi.cpp @@ -151,54 +151,9 @@ TEST(nntrainer_capi_nnmodel, compile_01_p) { /** * @brief Neural Network Model Compile Test */ -TEST(nntrainer_capi_nnmodel, compile_with_single_param_01_p) { - ml_train_model_h handle = NULL; - int status = ML_ERROR_NONE; - - ScopedIni s("capi_test_compile_with_single_param_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); - status = - ml_train_model_compile_with_single_param(handle, "loss=cross|epochs=2"); - EXPECT_EQ(status, ML_ERROR_NONE); - status = ml_train_model_destroy(handle); - EXPECT_EQ(status, ML_ERROR_NONE); -} - -/** - * @brief Neural Network Model Compile Test - */ -TEST(nntrainer_capi_nnmodel, construct_conf_01_n) { - ml_train_model_h handle = NULL; - int status = ML_ERROR_NONE; - std::string config_file = "/test/cannot_find.ini"; - status = ml_train_model_construct_with_conf(config_file.c_str(), &handle); - EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); -} - -/** - * @brief Neural Network Model Compile Test - */ -TEST(nntrainer_capi_nnmodel, construct_conf_02_n) { - ml_train_model_h handle = NULL; - int status = ML_ERROR_NONE; - - ScopedIni s("capi_test_compile_03_n", - {model_base, optimizer, dataset, inputlayer + "Input_Shape=1:1:0", - outputlayer}); - - status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle); - EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); -} - -/** - * @brief Neural Network Model Compile Test - */ TEST(nntrainer_capi_nnmodel, compile_02_n) { int status = ML_ERROR_NONE; - std::string config_file = "./test_compile_03_n.ini"; + std::string config_file = "./test_compile_02_n.ini"; status = ml_train_model_compile(NULL); EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); } @@ -206,7 +161,7 @@ TEST(nntrainer_capi_nnmodel, compile_02_n) { /** * @brief Neural Network Model Optimizer Test */ -TEST(nntrainer_capi_nnmodel, compile_05_p) { +TEST(nntrainer_capi_nnmodel, compile_03_p) { int status = ML_ERROR_NONE; ml_train_model_h model; @@ -271,7 +226,7 @@ TEST(nntrainer_capi_nnmodel, compile_05_p) { /** * @brief Neural Network Model Optimizer Test */ -TEST(nntrainer_capi_nnmodel, compile_06_n) { +TEST(nntrainer_capi_nnmodel, compile_04_n) { int status = ML_ERROR_NONE; ml_train_model_h model; @@ -349,18 +304,44 @@ TEST(nntrainer_capi_nnmodel, compile_06_n) { /** * @brief Neural Network Model Compile Test */ -TEST(nntrainer_capi_nnmodel, compile_with_single_param_01_n) { +TEST(nntrainer_capi_nnmodel, construct_conf_01_n) { + ml_train_model_h handle = NULL; + int status = ML_ERROR_NONE; + std::string config_file = "/test/cannot_find.ini"; + status = ml_train_model_construct_with_conf(config_file.c_str(), &handle); + EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); +} + +/** + * @brief Neural Network Model Compile Test + */ +TEST(nntrainer_capi_nnmodel, construct_conf_02_n) { + ml_train_model_h handle = NULL; + int status = ML_ERROR_NONE; + + ScopedIni s("capi_test_construct_conf_02_n", + {model_base, optimizer, dataset, inputlayer + "Input_Shape=1:1:0", + outputlayer}); + + status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle); + EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); +} + +/** + * @brief Neural Network Model Compile Test + */ +TEST(nntrainer_capi_nnmodel, compile_with_single_param_01_p) { ml_train_model_h handle = NULL; int status = ML_ERROR_NONE; - ScopedIni s("capi_test_compile_with_single_param_01_n", + ScopedIni s("capi_test_compile_with_single_param_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); status = - ml_train_model_compile_with_single_param(handle, "loss=cross epochs=2"); - EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); + ml_train_model_compile_with_single_param(handle, "loss=cross|epochs=2"); + EXPECT_EQ(status, ML_ERROR_NONE); status = ml_train_model_destroy(handle); EXPECT_EQ(status, ML_ERROR_NONE); } @@ -378,7 +359,7 @@ TEST(nntrainer_capi_nnmodel, compile_with_single_param_02_n) { status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle); EXPECT_EQ(status, ML_ERROR_NONE); status = - ml_train_model_compile_with_single_param(handle, "loss=cross,epochs=2"); + ml_train_model_compile_with_single_param(handle, "loss=cross epochs=2"); EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); status = ml_train_model_destroy(handle); EXPECT_EQ(status, ML_ERROR_NONE); @@ -391,54 +372,46 @@ TEST(nntrainer_capi_nnmodel, compile_with_single_param_03_n) { ml_train_model_h handle = NULL; int status = ML_ERROR_NONE; - ScopedIni s("capi_test_compile_with_single_param_02_n", + ScopedIni s("capi_test_compile_with_single_param_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); status = - ml_train_model_compile_with_single_param(handle, "loss=cross!epochs=2"); + ml_train_model_compile_with_single_param(handle, "loss=cross,epochs=2"); EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); status = ml_train_model_destroy(handle); EXPECT_EQ(status, ML_ERROR_NONE); } + /** - * @brief Neural Network Model Train Test + * @brief Neural Network Model Compile Test */ -TEST(nntrainer_capi_nnmodel, train_01_p) { +TEST(nntrainer_capi_nnmodel, compile_with_single_param_04_n) { ml_train_model_h handle = NULL; int status = ML_ERROR_NONE; - ScopedIni s("capi_test_train_01_p", - {model_base + "batch_size = 16", optimizer, - dataset + "-BufferSize", inputlayer, outputlayer}); + ScopedIni s("capi_test_compile_with_single_param_04_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); - - status = ml_train_model_compile(handle, NULL); - EXPECT_EQ(status, ML_ERROR_NONE); - - status = ml_train_model_run(handle, NULL); - EXPECT_EQ(status, ML_ERROR_NONE); - - /** Compare training statistics */ - nntrainer_capi_model_comp_metrics(handle, 3.911289, 2.933979, 10.4167); - + status = + ml_train_model_compile_with_single_param(handle, "loss=cross!epochs=2"); + EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); status = ml_train_model_destroy(handle); EXPECT_EQ(status, ML_ERROR_NONE); } - /** * @brief Neural Network Model Train Test */ -TEST(nntrainer_capi_nnmodel, train_with_single_param_01_p) { +TEST(nntrainer_capi_nnmodel, train_01_p) { ml_train_model_h handle = NULL; int status = ML_ERROR_NONE; - ScopedIni s( - "capi_test_train_with_single_param_01_p", - {model_base, optimizer, dataset + "-BufferSize", inputlayer, outputlayer}); + ScopedIni s("capi_test_train_01_p", + {model_base + "batch_size = 16", optimizer, + dataset + "-BufferSize", inputlayer, outputlayer}); status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle); EXPECT_EQ(status, ML_ERROR_NONE); @@ -446,12 +419,11 @@ TEST(nntrainer_capi_nnmodel, train_with_single_param_01_p) { status = ml_train_model_compile(handle, NULL); EXPECT_EQ(status, ML_ERROR_NONE); - status = - ml_train_model_run_with_single_param(handle, "epochs=2|batch_size=16"); + status = ml_train_model_run(handle, NULL); EXPECT_EQ(status, ML_ERROR_NONE); /** Compare training statistics */ - nntrainer_capi_model_comp_metrics(handle, 3.67021, 3.26736, 10.4167); + nntrainer_capi_model_comp_metrics(handle, 3.911289, 2.933979, 10.4167); status = ml_train_model_destroy(handle); EXPECT_EQ(status, ML_ERROR_NONE); @@ -472,7 +444,7 @@ 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("capi_test_train_01_p", + ScopedIni s("capi_test_train_03_n", {model_base + "batch_size = 16", optimizer, dataset + "-BufferSize", inputlayer, outputlayer}); @@ -489,12 +461,40 @@ TEST(nntrainer_capi_nnmodel, train_03_n) { /** * @brief Neural Network Model Train Test */ -TEST(nntrainer_capi_nnmodel, train_with_single_param_01_n) { +TEST(nntrainer_capi_nnmodel, train_with_single_param_01_p) { ml_train_model_h handle = NULL; int status = ML_ERROR_NONE; ScopedIni s( - "capi_test_train_with_single_param_01_n", + "capi_test_train_with_single_param_01_p", + {model_base, optimizer, dataset + "-BufferSize", inputlayer, outputlayer}); + + status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle); + EXPECT_EQ(status, ML_ERROR_NONE); + + status = ml_train_model_compile(handle, NULL); + EXPECT_EQ(status, ML_ERROR_NONE); + + status = + ml_train_model_run_with_single_param(handle, "epochs=2|batch_size=16"); + EXPECT_EQ(status, ML_ERROR_NONE); + + /** Compare training statistics */ + nntrainer_capi_model_comp_metrics(handle, 3.77080, 3.18020, 10.4167); + + status = ml_train_model_destroy(handle); + EXPECT_EQ(status, ML_ERROR_NONE); +} + +/** + * @brief Neural Network Model Train Test + */ +TEST(nntrainer_capi_nnmodel, train_with_single_param_02_n) { + ml_train_model_h handle = NULL; + int status = ML_ERROR_NONE; + + ScopedIni s( + "capi_test_train_with_single_param_02_n", {model_base, optimizer, dataset + "-BufferSize", inputlayer, outputlayer}); status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle); @@ -514,12 +514,12 @@ TEST(nntrainer_capi_nnmodel, train_with_single_param_01_n) { /** * @brief Neural Network Model Train Test */ -TEST(nntrainer_capi_nnmodel, train_with_single_param_02_n) { +TEST(nntrainer_capi_nnmodel, train_with_single_param_03_n) { ml_train_model_h handle = NULL; int status = ML_ERROR_NONE; ScopedIni s( - "capi_test_train_with_single_param_02_n", + "capi_test_train_with_single_param_03_n", {model_base, optimizer, dataset + "-BufferSize", inputlayer, outputlayer}); status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle); @@ -539,12 +539,12 @@ TEST(nntrainer_capi_nnmodel, train_with_single_param_02_n) { /** * @brief Neural Network Model Train Test */ -TEST(nntrainer_capi_nnmodel, train_with_single_param_03_n) { +TEST(nntrainer_capi_nnmodel, train_with_single_param_04_n) { ml_train_model_h handle = NULL; int status = ML_ERROR_NONE; ScopedIni s( - "capi_test_train_with_single_param_02_n", + "capi_test_train_with_single_param_04_n", {model_base, optimizer, dataset + "-BufferSize", inputlayer, outputlayer}); status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &handle); @@ -706,13 +706,13 @@ TEST(nntrainer_capi_nnmodel, addLayer_05_n) { /** * @brief Neural Network Model Add layer test */ -TEST(nntrainer_capi_nnmodel, addLayer_07_n) { +TEST(nntrainer_capi_nnmodel, addLayer_06_n) { int status = ML_ERROR_NONE; ml_train_model_h model = NULL; ml_train_layer_h layer = NULL; - ScopedIni s("capi_test_addLayer_07_n", + ScopedIni s("capi_test_addLayer_06_n", {model_base, optimizer, dataset, inputlayer, outputlayer}); status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &model); @@ -867,6 +867,46 @@ TEST(nntrainer_capi_nnmodel, getLayer_04_n) { } /** + * @brief Neural Network Model Get Layer Test + */ +TEST(nntrainer_capi_nnmodel, getLayer_05_n) { + int status = ML_ERROR_NONE; + + ml_train_model_h model; + ml_train_layer_h get_layer; + + std::string default_name = "inputlayer", modified_name = "renamed_inputlayer"; + char *default_summary, *modified_summary = nullptr; + + ScopedIni s("getLayer_05_p", {model_base, inputlayer}); + + status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &model); + EXPECT_EQ(status, ML_ERROR_NONE); + + status = + ml_train_model_get_summary(model, ML_TRAIN_SUMMARY_MODEL, &default_summary); + EXPECT_EQ(status, ML_ERROR_NONE); + + std::string default_summary_str(default_summary); + EXPECT_NE(default_summary_str.find(default_name), std::string::npos); + free(default_summary); + + status = ml_train_model_get_layer(model, default_name.c_str(), &get_layer); + EXPECT_EQ(status, ML_ERROR_NONE); + + status = ml_train_layer_set_property(get_layer, + ("name=" + modified_name).c_str(), NULL); + EXPECT_EQ(status, ML_ERROR_NONE); + + ///@todo need to fix bug (Unable to get renamed layer) + status = ml_train_model_get_layer(model, modified_name.c_str(), &get_layer); + EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); + + status = ml_train_model_destroy(model); + EXPECT_EQ(status, ML_ERROR_NONE); +} + +/** * @brief Neural Network Model Get Weight Test */ TEST(nntrainer_capi_nnmodel, getWeight_01) { @@ -923,46 +963,6 @@ TEST(nntrainer_capi_nnmodel, getWeight_01) { } /** - * @brief Neural Network Model Get Layer Test - */ -TEST(nntrainer_capi_nnmodel, getLayer_05_n) { - int status = ML_ERROR_NONE; - - ml_train_model_h model; - ml_train_layer_h get_layer; - - std::string default_name = "inputlayer", modified_name = "renamed_inputlayer"; - char *default_summary, *modified_summary = nullptr; - - ScopedIni s("getLayer_02_p", {model_base, inputlayer}); - - status = ml_train_model_construct_with_conf(s.getIniName().c_str(), &model); - EXPECT_EQ(status, ML_ERROR_NONE); - - status = - ml_train_model_get_summary(model, ML_TRAIN_SUMMARY_MODEL, &default_summary); - EXPECT_EQ(status, ML_ERROR_NONE); - - std::string default_summary_str(default_summary); - EXPECT_NE(default_summary_str.find(default_name), std::string::npos); - free(default_summary); - - status = ml_train_model_get_layer(model, default_name.c_str(), &get_layer); - EXPECT_EQ(status, ML_ERROR_NONE); - - status = ml_train_layer_set_property(get_layer, - ("name=" + modified_name).c_str(), NULL); - EXPECT_EQ(status, ML_ERROR_NONE); - - ///@todo need to fix bug (Unable to get renamed layer) - status = ml_train_model_get_layer(model, modified_name.c_str(), &get_layer); - EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER); - - status = ml_train_model_destroy(model); - EXPECT_EQ(status, ML_ERROR_NONE); -} - -/** * @brief Neural Network Model Optimizer Test */ TEST(nntrainer_capi_nnmodel, create_optimizer_01_p) { diff --git a/test/tizen_capi/unittest_tizen_capi_optimizer.cpp b/test/tizen_capi/unittest_tizen_capi_optimizer.cpp index 6ac32d5..f52da48 100644 --- a/test/tizen_capi/unittest_tizen_capi_optimizer.cpp +++ b/test/tizen_capi/unittest_tizen_capi_optimizer.cpp @@ -72,7 +72,7 @@ TEST(nntrainer_capi_nnopt, create_delete_04_n) { /** * @brief Neural Network Optimizer set Property Test (positive test) */ -TEST(nntrainer_capi_nnopt, setOptimizer_01_p) { +TEST(nntrainer_capi_nnopt, setProperty_01_p) { ml_train_optimizer_h handle; int status; status = ml_train_optimizer_create(&handle, ML_TRAIN_OPTIMIZER_TYPE_ADAM); @@ -87,7 +87,7 @@ TEST(nntrainer_capi_nnopt, setOptimizer_01_p) { /** * @brief Neural Network Optimizer Set Property Test (positive test) */ -TEST(nntrainer_capi_nnopt, setOptimizer_02_p) { +TEST(nntrainer_capi_nnopt, setProperty_02_p) { ml_train_optimizer_h handle; int status; status = ml_train_optimizer_create(&handle, ML_TRAIN_OPTIMIZER_TYPE_ADAM); @@ -103,7 +103,7 @@ TEST(nntrainer_capi_nnopt, setOptimizer_02_p) { /** * @brief Neural Network Optimizer Set Property Test (negative test) */ -TEST(nntrainer_capi_nnopt, setOptimizer_03_n) { +TEST(nntrainer_capi_nnopt, setProperty_03_n) { ml_train_optimizer_h handle; int status; status = ml_train_optimizer_create(&handle, ML_TRAIN_OPTIMIZER_TYPE_ADAM); @@ -118,7 +118,7 @@ TEST(nntrainer_capi_nnopt, setOptimizer_03_n) { /** * @brief Neural Network Optimizer Set Property Test (negative test) */ -TEST(nntrainer_capi_nnopt, setOptimizer_04_n) { +TEST(nntrainer_capi_nnopt, setProperty_04_n) { ml_train_optimizer_h handle = NULL; int status; @@ -131,7 +131,7 @@ TEST(nntrainer_capi_nnopt, setOptimizer_04_n) { /** * @brief Neural Network Optimizer Set Property Test (negative test) */ -TEST(nntrainer_capi_nnopt, setOptimizer_05_n) { +TEST(nntrainer_capi_nnopt, setProperty_05_n) { ml_train_optimizer_h handle = NULL; int status; status = ml_train_optimizer_create(&handle, ML_TRAIN_OPTIMIZER_TYPE_ADAM); @@ -143,7 +143,7 @@ TEST(nntrainer_capi_nnopt, setOptimizer_05_n) { /** * @brief Neural Network Optimizer Set Property Test (positive test) */ -TEST(nntrainer_capi_nnopt, setOptimizer_with_single_param_06_p) { +TEST(nntrainer_capi_nnopt, setProperty_with_single_param_01_p) { ml_train_optimizer_h handle; int status; status = ml_train_optimizer_create(&handle, ML_TRAIN_OPTIMIZER_TYPE_ADAM); @@ -158,7 +158,7 @@ TEST(nntrainer_capi_nnopt, setOptimizer_with_single_param_06_p) { /** * @brief Neural Network Optimizer Set Property Test (negative test) */ -TEST(nntrainer_capi_nnopt, setOptimizer_with_single_param_07_n) { +TEST(nntrainer_capi_nnopt, setProperty_with_single_param_02_n) { ml_train_optimizer_h handle; int status; status = ml_train_optimizer_create(&handle, ML_TRAIN_OPTIMIZER_TYPE_ADAM); @@ -173,7 +173,7 @@ TEST(nntrainer_capi_nnopt, setOptimizer_with_single_param_07_n) { /** * @brief Neural Network Optimizer Set Property Test (negative test) */ -TEST(nntrainer_capi_nnopt, setOptimizer_with_single_param_08_n) { +TEST(nntrainer_capi_nnopt, setProperty_with_single_param_03_n) { ml_train_optimizer_h handle; int status; status = ml_train_optimizer_create(&handle, ML_TRAIN_OPTIMIZER_TYPE_ADAM); -- 2.7.4