def test_evaluate_interface(self):
"""
<b>Description:</b>
Check IEvaluationTask class object initialization
<b>Input data:</b>
IEvaluationTask object
<b>Expected results:</b>
Test passes if IEvaluationTask object evaluate method raises NotImplementedError exception
"""
dataset = DatasetEntity()
configuration = ModelConfiguration(
configurable_parameters=ConfigurableParameters(
header="Test Header"),
label_schema=LabelSchemaEntity(),
)
model_entity = ModelEntity(configuration=configuration,
train_dataset=dataset)
with pytest.raises(NotImplementedError):
IEvaluationTask().evaluate(
ResultSetEntity(
model=model_entity,
ground_truth_dataset=dataset,
prediction_dataset=dataset,
))
def test_training_interface(self):
"""
<b>Description:</b>
Check ITrainingTask class object initialization
<b>Input data:</b>
ITrainingTask object
<b>Expected results:</b>
Test passes if ITrainingTask object methods raise NotImplementedError exception
"""
i_training_task = ITrainingTask()
dataset = DatasetEntity()
configuration = ModelConfiguration(
configurable_parameters=ConfigurableParameters(
header="Test Header"),
label_schema=LabelSchemaEntity(),
)
model_entity = ModelEntity(configuration=configuration,
train_dataset=dataset)
train_parameters = TrainParameters()
with pytest.raises(NotImplementedError):
i_training_task.save_model(model_entity)
with pytest.raises(NotImplementedError):
i_training_task.train(
dataset=dataset,
output_model=model_entity,
train_parameters=train_parameters,
)
with pytest.raises(NotImplementedError):
i_training_task.cancel_training()
def test_optimization_interface(self):
"""
<b>Description:</b>
Check IOptimizationTask class object initialization
<b>Input data:</b>
IOptimizationTask object
<b>Expected results:</b>
Test passes if IOptimizationTask object optimize method raises NotImplementedError exception
"""
dataset = DatasetEntity()
configuration = ModelConfiguration(
configurable_parameters=ConfigurableParameters(
header="Test Header"),
label_schema=LabelSchemaEntity(),
)
model_entity = ModelEntity(configuration=configuration,
train_dataset=dataset)
optimization_parameters = OptimizationParameters()
with pytest.raises(NotImplementedError):
IOptimizationTask().optimize(
optimization_type=OptimizationType.POT,
dataset=dataset,
output_model=model_entity,
optimization_parameters=optimization_parameters,
)
def metadata_item_with_model() -> MetadataItemEntity:
data = TensorEntity(
name="appended_metadata_with_model",
numpy=np.random.randint(low=0, high=255, size=(10, 15, 3)),
)
configuration = ModelConfiguration(
configurable_parameters=ConfigurableParameters(
header="Test Header"),
label_schema=LabelSchemaEntity(),
)
model = ModelEntity(configuration=configuration,
train_dataset=DatasetEntity())
metadata_item_with_model = MetadataItemEntity(data=data, model=model)
return metadata_item_with_model
def test_set_hyper_parameters(self):
"""
<b>Description:</b>
Check set_hyper_parameters() method
<b>Input data:</b>
Dummmy data
<b>Expected results:</b>
Test passes if incoming data is processed correctly
<b>Steps</b>
1. Checking parameters after setting
"""
env = environment()
header = "Test header"
description = "Test description"
visible_in_ui = False
id = ID(123456789)
hyper_parameters = ConfigurableParameters(header=header,
description=description,
visible_in_ui=visible_in_ui,
id=id)
env.set_hyper_parameters(hyper_parameters=hyper_parameters)
assert env.get_hyper_parameters().header == header
assert env.get_hyper_parameters().description == description
assert env.get_hyper_parameters().visible_in_ui == visible_in_ui
assert env.get_hyper_parameters().id == id
assert env.get_model_configuration(
).configurable_parameters.header == header
assert (env.get_model_configuration().configurable_parameters.
description == description)
assert (env.get_model_configuration().configurable_parameters.
visible_in_ui == visible_in_ui)
assert env.get_model_configuration().configurable_parameters.id == id
with pytest.raises(ValueError):
# ValueError: Unable to set hyper parameters, invalid input: 123
env.set_hyper_parameters(hyper_parameters="123")
def test_model_configuration(self):
"""
<b>Description:</b>
Check that ModelConfiguration correctly returns the configuration
<b>Input data:</b>
ConfigurableParameters, LabelSchemaEntity
<b>Expected results:</b>
Test passes if ModelConfiguration correctly returns the configuration
<b>Steps</b>
1. Check configuration params in the ModelConfiguration
"""
parameters = ConfigurableParameters(header="Test header")
label_schema = LabelSchemaEntity()
model_configuration = ModelConfiguration(
configurable_parameters=parameters, label_schema=label_schema)
assert model_configuration.configurable_parameters == parameters
assert model_configuration.label_schema == label_schema
def test_export_interface(self):
"""
<b>Description:</b>
Check IExportTask class object initialization
<b>Input data:</b>
IExportTask object
<b>Expected results:</b>
Test passes if IExportTask object export method raises NotImplementedError exception
"""
dataset = DatasetEntity()
configuration = ModelConfiguration(
configurable_parameters=ConfigurableParameters(
header="Test Header"),
label_schema=LabelSchemaEntity(),
)
model_entity = ModelEntity(configuration=configuration,
train_dataset=dataset)
with pytest.raises(NotImplementedError):
IExportTask().export(export_type=ExportType.OPENVINO,
output_model=model_entity)
def other_configuration(self):
parameters = ConfigurableParameters(header="Other test header")
label_schema = LabelSchemaEntity()
return ModelConfiguration(configurable_parameters=parameters,
label_schema=label_schema)