Throw error when load config failed

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -276,7 +276,7 @@ void bindTensor(py::module *m) {
     .def("get_op_connector_size", &ConfigManager::op_connector_size)
     .def("get_seed", &ConfigManager::seed)
     .def("get_monitor_sampling_interval", &ConfigManager::monitor_sampling_interval)
-    .def("load", [](ConfigManager &c, std::string s) { (void)c.LoadFile(s); });
+    .def("load", [](ConfigManager &c, std::string s) { THROW_IF_ERROR(c.LoadFile(s)); });
 
   (void)py::class_<Tensor, std::shared_ptr<Tensor>>(*m, "Tensor", py::buffer_protocol())
     .def(py::init([](py::array arr) {

mindspore/ccsrc/dataset/core/config_manager.cc

@@ -48,7 +48,7 @@ Status ConfigManager::FromJson(const nlohmann::json &j) {
 Status ConfigManager::LoadFile(const std::string &settingsFile) {
   Status rc;
   if (!Path(settingsFile).Exists()) {
-    RETURN_STATUS_UNEXPECTED("File is not found");
+    RETURN_STATUS_UNEXPECTED("File is not found.");
   }
   // Some settings are mandatory, others are not (with default).  If a setting
   // is optional it will set a default value if the config is missing from the file.
@@ -59,14 +59,11 @@ Status ConfigManager::LoadFile(const std::string &settingsFile) {
     rc = FromJson(js);
   } catch (const nlohmann::json::type_error &e) {
     std::ostringstream ss;
-    ss << "Client settings failed to load:\n" << e.what();
+    ss << "Client file failed to load:\n" << e.what();
     std::string err_msg = ss.str();
     RETURN_STATUS_UNEXPECTED(err_msg);
   } catch (const std::exception &err) {
-    std::ostringstream ss;
-    ss << "Client settings failed to load:\n" << err.what();
-    std::string err_msg = ss.str();
-    RETURN_STATUS_UNEXPECTED(err_msg);
+    RETURN_STATUS_UNEXPECTED("Client file failed to load.");
   }
   return rc;
 }

tests/ut/data/dataset/declient_filter.cfg

-{
-   "rowsPerBuffer": 10,
-}

tests/ut/python/dataset/test_filterop.py

@@ -390,7 +390,6 @@ def filter_func_Partial_0(col1, col2, col3, col4):
 
 # test with  row_data_buffer > 1
 def test_filter_by_generator_Partial0():
-    ds.config.load('../data/dataset/declient_filter.cfg')
     dataset1 = ds.GeneratorDataset(source=generator_mc_p0(), column_names=["col1", "col2"])
     dataset2 = ds.GeneratorDataset(source=generator_mc_p1(), column_names=["col3", "col4"])
     dataset_zip = ds.zip((dataset1, dataset2))
@@ -404,7 +403,6 @@ def test_filter_by_generator_Partial0():
 
 # test with  row_data_buffer > 1
 def test_filter_by_generator_Partial1():
-    ds.config.load('../data/dataset/declient_filter.cfg')
     dataset1 = ds.GeneratorDataset(source=generator_mc_p0(), column_names=["col1", "col2"])
     dataset2 = ds.GeneratorDataset(source=generator_mc_p1(), column_names=["col3", "col4"])
     dataset_zip = ds.zip((dataset1, dataset2))
@@ -419,7 +417,6 @@ def test_filter_by_generator_Partial1():
 
 # test with  row_data_buffer > 1
 def test_filter_by_generator_Partial2():
-    ds.config.load('../data/dataset/declient_filter.cfg')
     dataset1 = ds.GeneratorDataset(source=generator_mc_p0(), column_names=["col1", "col2"])
     dataset2 = ds.GeneratorDataset(source=generator_mc_p1(), column_names=["col3", "col4"])
 
@@ -454,7 +451,6 @@ def generator_big(maxid=20):
 
 # test with  row_data_buffer > 1
 def test_filter_by_generator_Partial():
-    ds.config.load('../data/dataset/declient_filter.cfg')
     dataset = ds.GeneratorDataset(source=generator_mc(99), column_names=["col1", "col2"])
     dataset_s = dataset.shuffle(4)
     dataset_f1 = dataset_s.filter(input_columns=["col1", "col2"], predicate=filter_func_Partial, num_parallel_workers=1)
@@ -473,7 +469,6 @@ def filter_func_cifar(col1, col2):
 # test with  cifar10
 def test_filte_case_dataset_cifar10():
     DATA_DIR_10 = "../data/dataset/testCifar10Data"
-    ds.config.load('../data/dataset/declient_filter.cfg')
     dataset_c = ds.Cifar10Dataset(dataset_dir=DATA_DIR_10, num_samples=100000, shuffle=False)
     dataset_f1 = dataset_c.filter(input_columns=["image", "label"], predicate=filter_func_cifar, num_parallel_workers=1)
     for item in dataset_f1.create_dict_iterator():